adoption research paper example

Understanding the concept of adoption: a qualitative analysis with adoptees and their parents

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The Department of Human Development and Family Studies focuses on the interactions among individuals, families, and their resources and environments throughout their lifespans. It consists of three majors: Child, Adult, and Family Services (preparing students to work for agencies serving children, youth, adults, and families); Family Finance, Housing, and Policy (preparing students for work as financial counselors, insurance agents, loan-officers, lobbyists, policy experts, etc); and Early Childhood Education (preparing students to teach and work with young children and their families).

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• College of Human Sciences ( parent college )
• Department of Child Development ( predecessor )
• Department of Family Environment ( predecessor )

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The purpose of this study was to gain a better understanding of children's and adults' experiences with adoption. This qualitative study used individual interviews to examine 25 participants---8 adoptive mothers and fathers, and their 5- to 14-year-old sons (n=5) and daughters ( n=4) adopted before 18 months. Data were collected using a phenomenological methodology and analysis of the data was guided by the following research questions: (a) What are children's and parents' overall experiences with adoption? (b) What is the social construction of adoption? (c) What do children understand about the concept of adoption and how do they construct that understanding? (d) How do language and word choices influence the concept of adoption? (e) What would you like others to know about adoption? Analysis followed steps defined by Moustakas and others and revealed five interactive themes that resonated among all families (a) parents' beliefs/experiences, (b) the need for education and change to promote adoption and positive adoption terminology, (c) communication, (d) children's understanding, and (e) identity. Limitations, future research possibilities, policy implications and implications for those who counsel, teach, and work with parents and children who have experienced adoption were identified.

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Ethical Considerations in Adoption Research: Navigating Confidentiality and Privacy Across the Adoption Kinship Network

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• 1 University of Massachusetts Amherst.
• 2 Boston College.
• PMID: 31598062
• PMCID: PMC6785193
• DOI: 10.1080/10926755.2018.1488328

Adoption research often includes multiple members of the adoption network, each of whom has distinctive perspectives. Participants may include adopted individuals and their siblings as well as adoptive parents, birth parents, and adoption professionals. Due to these multiple informants and the sensitivity of the topics explored in adoption research, researchers encounter several unique ethical concerns when working with populations impacted by adoption. The current paper addresses confidentiality and privacy issues that arise when conducting adoption research. Examples from a longitudinal study on openness in adoption are provided to highlight strategies that can be used to address these issues.

Keywords: adoption research; confidentiality; ethics; privacy.

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• R01 HD049859/HD/NICHD NIH HHS/United States

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Theses and dissertations on adoption.

Christian, C.L. (1995). Birthmother role adjustment in fully-disclosed, mediated and confidential adoptions. Unpublished masters thesis, University of Texas at Austin.

Fravel, D.L. (1995). Boundary ambiguity perceptions of adoptive parents experiencing various levels of openness in adoption. Unpublished doctoral dissertation, University of Minnesota.

Ross, N. M. (1995). Adoptive family processes that predict adopted child behavior and self-esteem. Unpublished master's thesis, University of Minnesota.

ten Broeke Balke, T. W. (1996). The percceptions of the role of birthfathers in adoption: A New Zealand perspective. Unpublished doctoral dissertation, University of Minnesota.

Gusukuma, I. (1997). Intercountry adoption: The experiences and adjustments of families adopting children from Latin America, China, and the United States. Unpublished doctoral dissertation: University of Texas at Austin.

Kohler, J.K. (1999). Adopted adolescents' preoccupation with adoption: The impact on adoptive family dynamics. Unpublished master's thesis, University of Minnesota.

Christian, C. L. (2000). Grief resolution of birthmothers: The impact of role development and varying degrees of openness. Unpublished doctoral dissertation, University of Texas at Austin.

Esau, A. L. (2000). Family contexts of birthmother identity and intimacy development. Unpublished doctoral dissertation, University of Minnesota.

van Dulmen, M. H. M. (2001). The family as context for the development of close peer relationships among adopted adolescents. Unpublished doctoral dissertation, University of Minnesota.

Dunbar, N. (2003). Typologies of adolescent adoptive identity: The influence of family context and relationships. Unpublished doctoral dissertation, University of Minnesota.

Von Korff, L. (2004). Openness arrangements and psychological adjustment in adolescent adoptees. Unpublished masters thesis, University of Minnesota.

Wolfgram, S. M. (2005). Predicting contact over time between adoptive parents and birthmothers in the open adoptive kinship network. Unpublished doctoral dissertation, University of Minnesota.

Perry, Y.V. (2006). "Comparing:" A Grounded theory of adoptive mothers' lay beliefs about genetics. Unpublished doctoral dissertation, University of Minnesota.

Newell, J.E. (2008). Openness to experience: Links to communicative and structural openness in adoptive kinship networks. Unpublished masters thesis, University of Minnesota.

Von Korff, L. (2008). Pathways to narrative adoptive identity formation in adolescence and emerging adulthood. Unpublished doctoral dissertation, University of Minnesota.

Skinner-Drawz, B. (2009). Adoptee information seeking: Changes between adolescence and emerging adulthood and the impact of adoption communicative openness. Unpublished doctoral dissertation, University of Minnesota.

Musante, D. (2010).  Family predictors of negative instability in adopted emerging adults. Unpublished masters thesis, University of Massachusetts Amherst.

Grant-Marsney, H. (2011). Adolescents’ attachment to adoptive parents: Predicting attachment styles in emerging adulthood. Unpublished masters thesis, University of Massachusetts Amherst.

Garber, K. (2013). “YOU were Adopted?!”: An Exploratory Analysis of Microaggressions Experienced By Adolescent Adopted Individuals. Unpublished masters thesis, University of Massachusetts Amherst.

Musante, D. (2014). Individuation as an adolescent developmental task: Associations with adoptee adjustment. Unpublished doctoral dissertation, University of Massachusetts Amherst.

Grant-Marsney, H. (2014). Emotion in adoption narratives: Links to close relationships in emerging adulthood. Unpublished doctoral dissertation, University of Massachusetts Amherst.

Lo, A. Y. H. (2017). Adoptive parenting cognitions, compatibility, and attachment among domestically adoptive families. Unpublished masters thesis, University of Massachusetts Amherst.

Cashen, K. K. (2017). Understanding relational competence in emerging adult adoptees: A new way to conceptualize competence in close relationships. Unpublished masters thesis, University of Massachusetts Amherst.

Altamari, D. K. (2018). Associations between peer attachment and positive adoption affect throughout adolescence and emerging adulthood. Unpublished honors thesis, University of Massachusetts Amherst.

Carlson, K. K. (2021). Use of mental health services and internalizing symptoms in domestic adoptees. Unpublished honors thesis, University of Massachusetts Amherst.

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• Research article
• Open access
• Published: 05 February 2021

Increasing adoption rates at animal shelters: a two-phase approach to predict length of stay and optimal shelter allocation

• Janae Bradley 1 &
• Suchithra Rajendran   ORCID: orcid.org/0000-0002-0817-6292 2 , 3  

BMC Veterinary Research volume  17 , Article number:  70 ( 2021 ) Cite this article

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Among the 6–8 million animals that enter the rescue shelters every year, nearly 3–4 million (i.e., 50% of the incoming animals) are euthanized, and 10–25% of them are put to death specifically because of shelter overcrowding each year. The overall goal of this study is to increase the adoption rates at animal shelters. This involves predicting the length of stay of each animal at shelters considering key features such as animal type (dog, cat, etc.), age, gender, breed, animal size, and shelter location.

Logistic regression, artificial neural network, gradient boosting, and the random forest algorithms were used to develop models to predict the length of stay. The performance of these models was determined using three performance metrics: precision, recall, and F1 score. The results demonstrated that the gradient boosting algorithm performed the best overall, with the highest precision, recall, and F1 score. Upon further observation of the results, it was found that age for dogs (puppy, super senior), multicolor, and large and small size were important predictor variables.

The findings from this study can be utilized to predict and minimize the animal length of stay in a shelter and euthanization. Future studies involve determining which shelter location will most likely lead to the adoption of that animal. The proposed two-phased tool can be used by rescue shelters to achieve the best compromise solution by making a tradeoff between the adoption speed and relocation cost.

As the problem of overpopulation of domestic animals continues to rise, animal shelters across the nation are faced with the challenge of finding solutions to increase the adoption rates. In the United States, about 6–8 million dogs and cats enter animal shelters every year, and 3–4 million of those animals are euthanized [ 1 ]. In other words, about 50% of the total canines and felines that enter animal shelters are put to death annually. Moreover, 10–25% of the total euthanized population in the United States is explicitly euthanized because of shelter overcrowding each year [ 2 ]. Though animal shelters provide incentives such as reduced adoption fees and sterilizing animals before adoption, only a quarter of total animals living in the shelter are adopted.

Animal adoption from shelters and rescues

There are various places to adopt an animal, and each potential owner must complete the adoption process and paperwork to take their new animal home [ 3 ]. Public and private animal shelters include animal control, city and county animal shelters, and police and health departments. Staff and volunteers run these facilities. Animals may also be adopted from a rescue organization, where pets are fostered in a home or a private boarding facility. These organizations are usually run by volunteers, and animals are viewed during local adoption events that are held at different locations, such as a pet store [ 3 ].

There could be several reasons for the euthanization of animals in a shelter, such as overcrowding, medical issues (ex. sick, disabled), or behavioral issues (ex. too aggressive). The causes for the overpopulation of animals include failure to spay or neuter animals leading to reckless breeding habits and abandonment or surrender of offspring, animal abandonment from owners who are no longer able to take care of or do not want the animal, and individuals still buying from pet stores [ 4 ]. With the finite room capacity for animals that are abandoned or surrendered, overpopulation becomes a key challenge [ 5 ]. Though medical and behavioral issues are harder to solve, the overpopulation of healthy adoptable animals in shelters is a problem that can be addressed through machine learning and predictive analytics.

Literature review

In this section, we describe the research conducted on animal shelters evaluating euthanasia and factors associated with animal adoption. The articles provide insights into factors that influence the length of stay and what characteristics influence adoption.

Studies have been conducted investigating the positive influence of pre-adoption neutering of animals on the probability of pet adoption [ 2 ]. The author investigated the impact of the cooperation of veterinary medical schools in increasing pet adoption by offering free sterilization. Results demonstrated that the collaboration between veterinary hospitals and local animal shelters decreased the euthanization of adoptable pets.

Hennessy et al. [ 6 ] conducted a study to determine the relationship between the behavior and cortisol levels of dogs in animal shelters and examined its effect in predicting behavioral issues after adoption. Shore et al. [ 7 ] analyzed the reasons for returning adopted animals by owners and obtained insights for these failed adoptions to attain more successful future approvals. The researchers found that prior failed adoption had led to longer-lasting future acceptances. They hypothesized that the failed adoptions might lead owners to discover their dog preferences by assessing their living situation and the type of animal that would meet that requirement.

Morris et al. [ 8 ] evaluated the trends in income and outcome data for shelters from 1989 to 2010 in a large U.S. metropolitan area. The results showed a decrease in euthanasia, adoption, and intake for dogs. For cats, a reduction in intake was observed until 1998, a decrease in euthanasia was observed until 2000, and the adoption of cats remained the same. Fantuzzi et al. [ 9 ] explored the factors that are significant for the adoption of cats in the animal shelter. The study investigated the effects of toy allocation, cage location, and cat characteristics (such as age, gender, color, and activity level). Results demonstrated that the more active cats that possessed toys and were viewed at eye level were more likely to impress the potential adopter and be adopted. Brown et al. [ 10 ] conducted a study evaluating the influence of age, breed, color, and coat pattern on the length of stay for cats in a no-kill shelter. The authors concluded that while color did not influence the length of stay for kittens, whereas gender, coat patterning, and breed were significant predictors for both cats and kittens.

Machine learning

Machine learning is one possible tool that can be used to identify risk factors for animal adoption and predict the length of stay for animals in shelters. Machine learning is the ability to program computers to learn and improve all by itself using training experience [ 11 ]. The goal of machine learning is to develop a system to analyze big data, quickly deliver accurate and repeatable results, and to adapt to new data independently. A system can be trained to make accurate predictions by learning from examples of desired input-output data. More specifically, machine learning algorithms are utilized to detect classification and prediction patterns from large data and to develop models to predict future outcomes [ 12 ]. These patterns show the relationship between the attribute variables (input) and target variables (output) [ 13 ].

Widely used data mining tasks include supervised learning, unsupervised learning, and reinforcement learning [ 14 ]. Unsupervised learning involves the use of unlabeled datasets to train a system for finding hidden patterns within the data [ 15 ]. Clustering is an example of unsupervised learning. Reinforcement learning is where a system is trained through direct interaction with the environment by trial and error [ 15 ]. Supervised learning encompasses classification and prediction using labeled datasets [ 15 ]. These classification and regression algorithms are used to classify the output variable with a discrete label or predict the outcome as a continuous or numerical value. Traditional algorithms such as neural networks, decision trees, and logistic regression typically use supervised learning. Figure  1 provides a pictorial of the steps for developing and testing a predictive model.

Pictorial Representation of Developing a Predictive Model

Contributions to the literature

Although prior studies have investigated the impact of several factors, such as age and gender, on the length of stay, they focus on a single shelter, rather than multiple organizations, as in this study. The goal of this study is to investigate the length of stay of animals at shelters and the factors influencing the rate of animal adoption. The overall goal is to increase adoption rates of pets in animal shelters by utilizing several factors to predict the length of stay. Machine learning algorithms are used to predict the length of stay of each animal based on numerous factors (such as breed, size, and color). We address several objectives in this study that are listed below.

Identify risk factors associated with adoption rate and length of stay

Utilize the identified risk factors from collected data to develop predictive models

Compare statistical models to determine the best model for length of stay prediction

Exploratory Data results

From Fig.  2 , it is evident that the return of dogs is the highest outcome type at 43.3%, while Fig.  3 shows that the adoption of cats is the highest outcome type at 46.1%. Both figures illustrate that the euthanization of both cats and dogs is still prevalent (~ 20%). The results from Table 1 demonstrate that the longest time spent in the shelter is at 355 days by a male cat that is adopted and a female dog that is euthanized. Observing the results, adoption has the lowest variance among all animal types compared to the other outcome types. Adopted male cats have the lowest variance for days spent in the shelter, followed by female dogs. Female cats that are returned have the highest variance for days spent in the shelter.

Distribution of Outcome Types for Dogs

Distribution of Outcome Types for Cats

Figure  4 shows a comparison of cats and dogs for the three different outcome types. It is observed from the data that there are more dogs returned than cats. From Fig.  5 , it is observed that the number of days a dog stays in the shelter decreases as the age increases. This is not expected, as it is predicted that the number of days in a shelter would be lower for younger dogs and puppies. This observation could be due to having more data points for younger dogs.

Comparison of Outcome Types for Cats and Dogs

Age vs. Days in Shelter for Cats and Dogs

Machine learning results

Examining Table 2 , it is clear that the most proficient predictive model is developed by the gradient boosting algorithm for this dataset, followed by the random forest algorithm. The logistic regression algorithm appears to perform the worst with low precision, recall, and F1 score performance metrics for all categories of length of stay. For the prediction of low length of stay in a shelter, the random forest algorithm is the best performing model in comparison to the others at around 64–70% performance for precision, recall, and F1 score. The ANN algorithm is found to be the best when evaluating the precision and F1 score for medium length of stay, while the random forest algorithm is better for assessing recall. However, the performance of these models in predicting the medium length of stay for the given dataset is low for all three-performance metrics. The gradient boosting algorithm performs the best when predicting the high length of stay. Finally, the gradient boosting and random forest algorithms perform well when predicting the very high length of stay at around 70–80%.

Results from Table 2 also demonstrate that the model developed from the gradient boosting algorithm has a higher performance when predicting the high length of stay that leads to adoption, and when the outcome is euthanization. Evaluating the average of all three-performance metrics for all algorithms, the gradient boosting is the most proficient model at almost 60%, while logistic regression appears to be the worst. Table 2 also provides the computational time for each machine learning algorithm. For the given dataset, logistic regression runs the fastest at 9.41 s, followed by gradient boosting, artificial neural network, and finally, random forest running the longest. The gap in the performance measure ( pm ) is calculated by \( \frac{p{m}_{best}-p{m}_{worst}}{p{m}_{best}} \) , and is nearly 34, 39, and 32% for precision, recall, and F1 score, respectively.

Table 3 provides information on the top features or factors from each machine learning algorithm. Observing the table, we find that age (senior, super senior, and puppy), size (large and small), and color (multicolor) has a significant impact or influence on the length of stay. Specifically, we observe that older-aged animals (senior and/or super senior) appear as a significant factor for every algorithm. For the artificial neural network, older age is the #2 and #3 predictor, and super senior is the #2 predictor for the gradient boosting algorithm. Large and small-sized animals are also observed to be important features, as both are shown as the #1 predictor in the gradient boosting and ANN algorithms. The results also demonstrate that gender, animal type, other colors besides multicolor, middle age, and medium-sized animals did not significantly impact the length of stay.

Results from our study provided information on what factors are significant in influencing length of stay. Brown et al. [ 10 ] conducted research that found that age, breed designation, coat color, and coat pattern influenced the length of stay for cats in animal shelters. Similar to these studies, observations from our study also suggest that age and color have a significant impact or influence on the length of stay.

Determining which algorithm will develop the best model for the given set of data is critical to predict the length of stay and minimize the chances of euthanization. The goal of predictive analytics is to develop a model that best approximates the true mapping function for the relationship between the input and output variables. To approximate this function, parametric or non-parametric algorithms can be used. Parametric algorithms simplify the unknown function to a known form. Non-parametric algorithms do not make assumptions about the structure of the mapping function, allowing free learning of any functional form. In this study, we utilize both parametric (logistic regression and artificial neural network) and non-parametric (random forest and gradient boosting) algorithms on the given data. Observing the results from Table 2 , the gradient boosting and random forest (non-parametric algorithms) perform the best on the dataset. It is observed from the results that using a non-parametric approach leads to a better approximation of the true mapping function for the given records. These results also support prior studies on parametric versus non-parametric methods. Neely et al. [ 16 ] detailed the theoretical superiority of non-parametric algorithms for detecting pharmacokinetic and pharmacodynamic subgroups in a study population. The author suggests this superiority comes from the lack of assumptions made about the distribution of parameter values in a dataset. Bissantz et al. [ 17 ] discussed a resampling algorithm that evaluates the deviations between parametric and non-parametric methods to be noise or systematic by comparing parametric models to a non-parametric “supermodel”. Results demonstrate the non-parametric model to be significantly better. The use of algorithms that do not approximate the true function of the relationship between input and output provides better performance results for this application as well.

Current literature also supports the use of ensemble methods to increase prediction accuracy and performance. Dietterich [ 18 ] discussed the ongoing research into developing good ensemble methods as well as the discovery that ensemble algorithms are often more accurate than individual algorithms that are used to create them. Pandey, and S, T [ 19 ]. conducted a study to compare the accuracy of ensemble methodology on predicting student academic performance as research has demonstrated better results for composite models over a single model. This study applied ensemble techniques on learning algorithms (AdaBoost, Random Forest, Rotation Forest, and Bagging). For our study with the given records, the results support this claim. Both the gradient boosting and random forest algorithms are ensemble algorithms and performed the best on the animal shelter data.

Results from Table 2 demonstrate the best performance of the gradient boosting and random forest algorithm when the length of stay was classified as very high or the animal was euthanized. This is beneficial as the models can predict long stays where the outcome is euthanasia. This can lead to shelters identifying at-risk animals and implementing methods and solutions to ensure their adoption. These potential methods are the second phase of this research study, which will involve relocating animals to shelters where they will more likely be adopted. This phase is discussed in the future directions section.

Studies have been conducted evaluating euthanasia-related stress on workers (e.g., [ 1 ]). In other words, overpopulation not only leads to euthanasia but can, in turn, cause mental and emotional problems for the workers. For instance, Reeve et al. [ 20 ] evaluated the strain related to euthanasia among animal workers. Results demonstrated that euthanasia related strain was prevalent, and an increase in substance abuse, job stress, work causing family conflict, complaints, and low job satisfaction was observed. Predicting the length of stay for animals will aid in them being more likely to be adopted and will lead to fewer animals being euthanized, adding value not only to animals finding a home but also less stress on the workers.

The approach developed in this paper could be beneficial not only to reduce euthanasia but also to reduce overcrowding in shelters operated in countries where euthanasia of healthy animals is illegal, and all animals must be housed in shelters until adoption (or natural death). It is essential to develop an information system for a collaborative animal shelter network in which the entities can coordinate with each other, exchanging information about the animal inventory. Another benefit of this study is that it investigates applying machine learning to the animal care domain. Previous studies have looked into what factors influence the length of stay; however, this study utilizes these factors in addition to classification algorithms to predict how long an animal will stay in the shelter. Moreover, the use of a prescriptive analytics approach is discussed in this paper, where the predictions made by the machine learning algorithms will be used along with a goal programming model to decide in what shelter is an animal most likely to be adopted.

Limitations of this study include lack of behavioral data, limited sample size, and the use of simple algorithms. The first limitation, lack of behavioral data of the animal during intake and outcome, would be beneficial to develop a more comprehensive model. Though behavioral problems are harder to solve, having data would provide insight into how long these animals with behavioral issues are staying in shelters and what the outcome is. Studies have shown that behavioral problems play a significant role in preventing bonding between owners and their animals and one of the most common reasons cited for animal surrender [ 21 , 22 ]. These behavioral problems can include poor manners, too much energy, aggression, and destruction of the household. Dogs surrendered to shelters because of behavioral issues have also been shown to be less likely to be adopted or rehomed, and the ones that are adopted are more likely to be returned [ 21 ]. Studies have also been conducted to evaluate the effect of the length of time on the behavior of dogs in rescue shelters [ 23 , 24 , 25 ]. Most of them concluded that environmental factors led to changes in the behavior of dogs and that a prolonged period in a shelter may lead to unattractive behavior of dogs to potential owners. Acquiring information on behavioral problems gives more information for the algorithm to learn when developing the predictive model. This allows more in-depth predictions to be made on how long an animal will stay in a shelter, which could also aid in adoption. This approach can be used to shorten the length of stay, which makes sure that healthy animals are not developing behavioral problems in the shelters. It is not only crucial for the animal to be adopted, but also that the adoption is a good fit between owner and pet. Shortening the length of stay would also lessen the chance that the animal will be returned by the adopter because of behavior. Having this information will also allow shelters to find other shelters close by where animals with behavioral issues are more likely to be adopted. To overcome this limitation of the lack of data on behavioral problems, behavioral issues will be used as a factor and will be specifically asked for when acquiring data from shelters.

Another limitation includes collecting more data from animal shelters across the United States, allowing for more representative data to be collected and inputted into these algorithms. However, this presents a challenge due to most shelters being underfunded and low on staff. Though we reached out to shelters, most replied that they lacked the resources and staff to provide the information needed. Future work would include applying for funding to provide a stipend to staff for their assistance in gathering the data from respective shelters. With more data, the algorithm has more information to learn on, which could improve the performance metrics of the predictive models developed. There may also be other factors that show to be significant as more data is collected.

Finally, the last limitation is the use of simpler algorithms. This study considers basic ML algorithms. Nevertheless, in recent years, there has been development in the ML field of more complex networks. For instance, Zhong et al. [ 26 ] proposed a novel reinforcement learning method to select neural blocks and develop deep learning networks. Results demonstrated high efficiency in comparison to most of the previous deep network search approaches. Though only four algorithms were considered, future work would investigate deep learning networks, as well as bagging algorithms. Using more complex algorithms could ensure that if intricate patterns in the data are present, the algorithm can learn them.

Future direction

Phase 2: goal programming approach for making relocation decisions.

Using the information gathered in this study, we can predict the type of animals that are being adopted the most in each region and during each season of the year. To accomplish this, we utilize a two-phase approach. The first phase was leveraging the machine learning algorithms to predict the length of stay of each animal based on numerous factors (such as breed, size, and color). Phase-2 involves determining the best shelter to transport adoptable animals to increase the adoption rates, based on several conflicting criteria. This criterion includes predicted length of stay from phase-1, the distance between where the animal is currently housed and the potential animal shelters, transportation costs, and transportation time. Therefore, our goal is to increase adoption rates of pets in animal shelters by utilizing several factors to predict the length of stay, as well as determine the optimal animal shelter location where the animal will have the least amount stay in a shelter and most likely be adopted.

After predicting the length of stay of an incoming animal that is currently housed in the shelter l ′ using the machine learning algorithms, the next phase is to evaluate the potential relocation options for that animal. This strategic decision is specifically essential if the length of stay of the animal at its current location is high/very high. Nevertheless, while making this relocation decision, it is also necessary to consider the cost of transporting the animal between the shelters. For instance, if a dog is brought into a shelter in Houston, Texas, and is estimated to have a high/very high length of stay. Suppose if the dog is predicted to have a low length of stay at New York City and a medium length of stay at Oklahoma City, then a tradeoff has to be made between the relocation cost and the adoption speed. The objectives, length of stay, and relocation costs are conflicting and have to be minimized. Phase-2 attempts to yield a compromise solution that establishes a trade-off between these two criteria.

Goal programming (GP) is a widely used approach to solve problems involving multiple conflicting criteria. Under this method, each objective function is assigned as a goal, and a target value is specified for the individual criterion [ 27 ]. These target numbers can be fulfilled by the model with certain deviations, while the objective of the GP model is to minimize these deviations. Pertaining to this study, the desired values for the length of stay and relocation cost is pre-specified in the model and can be fulfilled with deviations. The GP model attempts to minimize these deviations. Thus, this technique attempts to produce a solution that is as close as possible to the targets, and the model solutions are referred to as the “most preferred solution” by prior studies (e.g., [ 28 , 29 ]).

As mentioned earlier, the primary task to be completed using this phase-2 goal programming approach is the relocation decisions considering the adoption speed and the cost of transporting the animal from the current location.

Model notations

Goal programming model formulation, goal constraints.

Objective 1: Minimize the overall length of stay of the animal under consideration (Eq. 1 ).

Goal constraint for objective 1: The corresponding goal constraint of objective 2 is given using Equation [ 30 ].

Objective 2: Minimize the overall relocation cost for transporting the animal under consideration (Eq. 3 ).

Goal constraint for objective 2: The corresponding goal constraint of objective 2 is given using Equation [ 18 ].

Hard constraints

Equation [ 9 ] ensures that the animal can be assigned to only one shelter.

The animal can be accommodated in shelter l only if there are a shelter capacity and type for that particular animal size category, and this is guaranteed using constraint [ 31 ]. It is important to note that both y and s are input parameters , whereas l is the set of shelters.

Equation [ 21 ] sets an upper limit on the length of stay category if the shelter l is assigned as the destination location. This prevents relocating animals to a shelter that might potentially have a high or very high length of stay.

Similarly, Equation [ 32 ] sets an upper limit on the relocation cost, if the shelter l is assigned as the destination location. This prevents relocating animals to a very far location. The current shelter location, l ′ , that is hosting the animal is an input parameter.

Objective function

Since the current problem focuses on minimizing the expected length of stay and relocation cost, the objective function of the goal programming approach is to reduce the sum of the weighted positive deviations given in Equations ([ 18 , 30 ], as shown in Equation [ 6 ].

where w g is the weight assigned for each goal g .

It is necessary to scale the deviation (since the objectives have different magnitudes as well as units) to avoid a biased solution.

If the scaling factors are represented by f g for goal g , then the scaled objective function is given in Equation [ 14 ].

Using this goal programming approach, the potential relocation options are evaluated considering the length of stay from phase-1. This phase-2 goal programming approach is useful, especially if the length of stay of the animal at its current location is high/very high, and a trade-off has to be made between relocation cost and length of stay. Phase-2 acts as a recommendation tool for assisting administrators with relocation decisions.

Nearly 3–4 million animals are euthanized out of the 6–8 million animals that enter shelters annually. The overall objective of this study is to increase the adoption rates of animals entering shelters by using key factors found in the literature to predict the length of stay. The second phase determines the best shelter location to transport animals using the goal programming approach to make relocation decisions. To accomplish this objective, first, the data is acquired from online sources as well as from numerous shelters across the United States. Once the data is acquired and cleaned, predictive models are developed using logistic regression, artificial neural network, gradient boosting, and random forest. The performance of these models is determined using three performance metrics: precision, recall, and F1 score.

The results demonstrate that the gradient boosting algorithm performed the best overall, with the highest precision, recall, and F1 score. Followed closely in second is the random forest algorithm, then the artificial neural network, and then finally, the logistic regression algorithm is the worst performer. We also observed from the data that the gradient boosting performed better when predicting the high or very high length of stay. Further observing the results, it is found that age for dogs (e.g., puppy, super senior), multicolor, and large and small size are important predictor variables.

The findings from this study can be utilized to predict how long an animal will stay in a shelter, as well as minimize their length of stay and chance of euthanization by determining which shelter location will most likely lead to the adoption of that animal. For future studies, we will implement phase 2, which will determine the best shelter location to transport animals using the goal programming approach to make relocation decisions.

Data description

A literature review is conducted to determine the factors that might potentially influence the length of stay for animals in shelters. These factors include gender, breed, age, and several other variables that are listed in Table 4 . These features will be treated as input variables for the machine learning algorithms. Overall, there are eight input or predictor variables and one output variable, which is the length of stay.

Animal shelter intake and outcome data are publicly made available by several state/city governments on their website (e.g., [ 33 , 34 ]), specifically in several southern and south-western states. These online sources provide datasets for animal shelters from Kentucky (150,843 data rows), California (334,016), Texas (155,115), and Indiana (4132). Since there is no nationwide database for animal shelters, information is also collected through individual animal shelters that conduct euthanization of animals. We contacted over 100 animal shelters across the United States and inquired for data on the factors mentioned in Table 4 . We received responses from 20 of the animal shelters that were contacted. Most responses received stated there was not enough staff or resources to be able to provide this information. From the responses that were received back, only four shelters were able to provide any information. Of those four, only two of the datasets contained the factors and information needed, which are Colorado (8488 data rows) and Arizona (4, 667 data rows).

The data that is collected from the database and animal shelters included information such as animal type, intake and outcome date, gender, color, breed, and intake and outcome status (behavior of animal entering the shelter and behavior of animal at outcome type). These records also included information on several types of animals, such as dogs, cats, birds, rabbits, and lizards. For this study, the focus is on dogs and cats. After filtering through these records, we found that only California, Kentucky, Colorado, Arizona, and Indiana had all of the factors needed for the study. Upon downloading data from the database and receiving data from the animal shelters, the acquired data underwent data integration, data transformation, and data cleaning (as detailed in Fig.  1 ). After data pre-processing, there are over 113,000 animal records.

Data cleaning methods

Next, data cleaning methods are utilized to detect discrepancies in the data, such as missing values, erroneous data, and inconsistencies. Data cleaning is an essential step for obtaining unbiased results [ 35 , 36 ]. In other words, identifying and cleaning erroneous data must be performed before inputting the data into the algorithm as it can significantly impact the output results.

The following is a list of commonly used data cleaning techniques in the literature [ 11 ]:

Substitution with Median: Missing or incorrect data are replaced with the median value for that predictor variable.

Substitution with a Unique Value: Erroneous data are replaced with a value that does not fall within the range that the input variables can accept (e.g., a negative number)

Discard Variable and Substitute with a Median: When an input variable has a significant number of missing values, these values are removed from the dataset, and the features that remain with missing or erroneous values are replaced with the median.

Discard Variable and Substitute with a Unique Value: Input variables with a significant number of missing values are removed from the dataset, and the features that remain with missing or erroneous values are coded as − 1.

Remove Incomplete Rows Entirely: Incomplete Rows are removed from the dataset.

Data preprocessing

Some animal breeds are listed in multiple formats and are changed to maintain uniformity. An example of this is a Russian Blue cat, which is formatted in several ways such as “Russian”, “Russian Blue”, and “RUSSIAN BLUE”. Animals with multiple breeds such as “Shih Tzu/mix” or “Shih Tzu/Yorkshire Terr” are classified as the first breed listed. Other uncommon breeds are classified as “other” for simplicity. Finally, all animal breeds are summarized into three categories (small, medium, or large) using the American Kennel Clubs’ breed size classification [ 37 ]. Part of the data cleansing process also includes categorizing multiple colors found throughout the sample size into five distinct color categories (brown, black, blue, white, and multicolor). We classified age into five categories for dogs and cats (puppy or kitten, adolescent, adult, senior, super senior). The puppy or kitten category includes data points 0–1 year, adolescence includes data points 2–3 years old, adulthood includes animals 4–7 years of age, and senior animals are 8–10 years of age. Any animal that is older than ten years are categorized as a super senior, based on the recommendations provided in Wapiti Labs [ 38 ].

As mentioned previously, the output variable is the length of stay and is classified as low, medium, high, and very high/euthanization. The length of stay is calculated by taking the difference between the intake date and outcome date. To remove erroneous data entries and special cases, the number of days in the animal shelter is also capped at a year. The “low” category represents animals that are returned (in which case, they are assigned the days in the shelter as 0) or spent less than 8 days before getting adopted. It is important to keep these animals at the shelter so that the owner may find them or they are transferred to their new homes. Animals that stayed in a shelter for 9–42 days and are adopted are categorized as “medium” length of stay. The “high” category is given to animals that stayed in the shelter for 43–365 days. Finally, animals that are euthanized are categorized as “very high”.

After integrating all data points from each animal shelter, the sample size includes 119,691 records. After the evaluation of these data points, 5436 samples are found to have miscellaneous (such as a negative length of stay) or missing values. After applying data cleaning techniques, the final cleaned dataset includes 114,256 data points, with 50,466 cat- and 63,790 dog-records.

Machine learning algorithms to predict the length of stay

The preprocessed records are then separated into training and testing datasets based on the type of classification algorithm used. Studies have demonstrated the need for testing and comparing machine learning algorithms, as the performance of the models depends on the application. While an algorithm may develop a predictive model that performs well in one application, it may not be the best performing model for another. A comparison between the statistical models is conducted to determine the overall best performing model. In this section, we provide a description as well as the advantages of each classification algorithm that is utilized in this study.

Logistic regression

Logistic regression (LR) is a machine learning algorithm that is used to understand the probability of the occurrence of an event [ 39 ]. It is typically used when the model output variable is binary or categorical (see Fig.  6 ), unlike linear regression, where the dependent variable is numeric [ 40 ]. Logistic regression involves the use of a logistic function, referred to as a “sigmoid function” that takes a real-valued number and maps it into a value between 0 and 1 [ 41 ]. The probability that the length of stay of the animal at a specific location will be low, medium, high, or very high, is computed using the input features discussed in Table 4 .

Pictorial Representation of the Logistic Regression Algorithm

The linear predictor function to predict the probability that the animal in record i has a low, medium, high, and very high length of stay categories is given by Equations ( 11 ) –[ 3 ], respectively.

Where β v , l is a set of multinomial logistic regression coefficients for variable v of the length of stay category l , and x v , i is the input feature v corresponding to data observation i .

Artificial neural network

Artificial Neural Network (ANN) algorithms were inspired by the brain’s neuron, which transmits signals to other nerve cells [ 40 , 42 ]. ANN’s were designed to replicate the way humans learn and were developed to imitate the operational sequence in which the body sends signals in the nervous system [ 43 ]. In an ANN, there exists a network structure with directional links connecting multiple nodes or “artificial neurons”. These neurons are information-processing units, and the ties that connect them represent the relationship between each of the connected neurons. Each ANN consists of three layers - the input layer, hidden layer, and the output layer [ 32 , 44 ]. The input layer is where each of the input variables is fed into the artificial neuron. The neuron will first calculate the sum of multiple inputs from the independent variables. Each of the connecting links (synapses) from these inputs has a characterized weight or strength that has a negative or positive value [ 32 ]. When new data is received, the synaptic weight changes, and learning will occur. The hidden layer learns the relationship between the input and output variables, and a threshold value determines whether the artificial neuron will fire or pass the learned information to the output layer, as shown in Fig.  7 . Finally, the output layer is where labels are given to the output value, and backpropagation is used to correct any errors.

Pictorial Representation of the Artificial Neural Networks

Random Forest

The Random Forest (RF) algorithm is a type of ensemble methodology that combines the results of multiple decision trees to create a new predictive model that is less likely to misclassify new data [ 30 , 45 ]. Decision Trees have a root node at the top of the tree that consists of the attribute that best classifies the training data. The attribute with the highest information gain (given in Eq. 16 ) is used to determine the best attribute at each level/node. The root node will be split into more subnodes, which are categorized as a decision node or leaf node. A decision node can be divided into further subnodes, while a leaf node cannot be split further and will provide the final classification or discrete label. RF algorithm uses mtree and ntry as the two main parameters in developing the multiple parallel decision trees. Mtree specifies how many trees to train in parallel, while ntry defines the number of independent variables or attributes to choose to split each node [ 30 ].. The majority voting from all parallel trees gives the final prediction, as given in Fig.  8 .

Pictorial Representation of the Random Forest Algorithm

Gradient boosting

Boosting is another type of ensemble method that combines the results from multiple predictive algorithms to develop a new model. While the RF approach is built solely on decision trees, boosting algorithms can use various algorithms such as decision trees, logistic regression, and neural networks. The primary goal of boosting algorithms is to convert weak learners into stronger ones by leveraging weighted averages to identify “weak classifiers” [ 31 ]. Samples are assigned an initial uniformed weight, and when incorrectly labeled by the algorithm, a penalty of an increase in weight is given [ 46 ]. On the other hand, samples that are correctly classified by the algorithm will decrease in weight. This process of re-weighing is done until a weighted vote of weak classifiers is combined into a robust classifier that determines the final labels or classification [ 46 ]. For our study, gradient boosting (GB) will be used on decision trees for the given dataset, as illustrated in Fig.  9 .

Pictorial Representation of Boosting Algorithm

Machine learning model parameters

The clean animal shelter data is split into two datasets: training and testing data. These records are randomly placed in the two groups to train the algorithms and to test the model developed by the algorithm. 80% of the data is used to train the algorithm, while the other 20% is used to test the predictive model. To avoid overfitting, a tenfold cross-validation procedure is used on the training data. There are no parameters associated with the machine learning of logistic regression algorithms. However, a grid search method is used to tune the parameters of the random forest, gradient boosting, and artificial neural network algorithms. This allows the best parameter in a specific set to be chosen by running an in-depth search by the user during the training period.

The number of trees in the random forest and gradient boosting algorithms is changed from 100 to 1000 in increments of 100. A learning rate of 0.01, 0.05, and 0.10 is used based on the recommendations of previous studies [ 47 ]. The minimum observations for the trees’ terminal node are set to vary from 2 to 10 in increments of one, while the splitting of trees varies from 2 to 10 in increments of two. A feed-forward method is used to develop the predictive model using the artificial neural network algorithm. The feed-forward algorithm consists of three layers (input, hidden, output) as well as backpropagation learning. The independent and dependent variables represent the input and output layers. Since the input and output layers are already known, an optimal point is reached for the number of nodes when between 1 and the number of predictors. This means that for our study, the nodes of the hidden layer vary from 1 to 8. The learning rate values used to train the ANN are 0.01, 0.05, and 0.10.

To find the optimal setting for each machine learning algorithm, a thorough search of their corresponding parameter space is performed.

Performance measures

In this study, we use three performance measures to evaluate the ability of machine learning algorithms in developing the best predictive model for the intended application. The measures considered are precision, F1 score, and sensitivity/recall to determine the best model given the inputted data samples. Table 5 provides a confusion matrix to define the terms used for all possible outcomes.

Precision evaluates the number of correct, true positive predictions by the algorithm while still considering the incorrectly predicted positive when it should have been negative (Eq. 17 ). By having high precision, this means that there is a low rate of false positives or type I error. Sensitivity or recall evaluates the number of true positives that are correctly predicted by the algorithm while considering the incorrectly predicted negative when it should have been positive (Eq. 18 ). Recall is a good tool to use when the focus is on minimizing false negatives (type II error). F1 score (shown in Eq. 19 ) evaluates both type I and type II errors and assesses the ability of the model to resist false positives and false negatives. This performance metric evaluates the robustness (low number of missed classifications), as well as the number of data points that are classified correctly by the model.

Availability of data and materials

Most of the datasets used and/or analyzed during the current study were publicly available online as open source data. The data were available in the website details given below:

https://data.bloomington.in.gov/dataset

https://data.louisvilleky.gov/dataset

https://data.sonomacounty.ca.gov/Government

We also obtained data from Sun Cities 4 Paws Rescue, Inc., and the Rifle Animal Shelter. No administrative permission was required to access the raw data from these shelters.

Abbreviations

Logistic Regression

Artificial Neural Network

Gradient Boosting

Goal Programming

Coefficient of Variation

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Acknowledgments

We would like to thank the Sun Cities 4 Paws Rescue, Inc., and the Rifle Animal Shelter for providing the length of stay reports in order to complete this study.

This research was not funded by any agency/grant.

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Janae Bradley

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JB performed data mining, data cleaning and analyses of the animal shelter data and machine learning algorithms. JB was also a major contributor in writing the manuscript. SR performed data mining, cleaning, and analyses of the machine learning algorithms, as well as the goal programming. All authors have read and approved the final manuscript.

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Bradley, J., Rajendran, S. Increasing adoption rates at animal shelters: a two-phase approach to predict length of stay and optimal shelter allocation. BMC Vet Res 17 , 70 (2021). https://doi.org/10.1186/s12917-020-02728-2

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Department of Agricultural, Food, and Resource Economics Innovation Lab for Food Security Policy, Research, Capacity and Influence

Adoption of Sustainable Agricultural Intensification Practices and their Welfare Impacts: Comparative Evidence from Malawi, Uganda and Ethiopia

May 14, 2024 - Anderson Gondwe, Lemekezani K. Chilora, Dinah Salonga, Aleksandr Michuda and Kristin Davis

Sustainable intensification practices are popular interventions for enhancing soil fertility and crop yield, and eventually improving household income and food security. Using the Living Standards Measurement Study - Integrated Surveys on Agriculture panel data from Ethiopia, Malawi, and Uganda, we conduct a multi-country comparative analysis of the adoption of sustainable intensification practices and their impacts on food and nutritional security. While most studies use the sex of the household head to define gender, we base our gender variable on decision-making: male, female, and joint households' decision-making at a farm level. We use multinomial logit, multinomial endogenous switching regression and multinomial endogenous treatment effects models to account for selection bias and endogeneity originating from both observed and unobserved heterogeneity. Our analysis shows that adoption of sustainable intensification practices is impacted household size, wealth, livestock ownership, agroecological zones, and gender decision-making at a farm level. Our econometric analysis reveals that the relationship between the adoption of sustainable intensification practices and households' food and nutritional security varies by country, confirming the importance of considering country-specific contexts and practices when designing agricultural interventions. Policymakers should consider promoting the adoption of sustainable intensification practices as they have shown to have a positive impact on food and nutritional security. Sustainable intensification practices s, along with training programs for farmers, are crucial for enhancing knowledge and resource availability to implement sustainable intensification practices and improve food and nutrition security effectively. There is a need to increase investments in agricultural research, extension services, and climate-smart agriculture.

 Sustainable intensification practices, welfare, multinomial logit, multinomial endogenous switching regression and multinomial endogenous treatment effects

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• 09 May 2024

Cubic millimetre of brain mapped in spectacular detail

• Carissa Wong

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Rendering based on electron-microscope data, showing the positions of neurons in a fragment of the brain cortex. Neurons are coloured according to size. Credit: Google Research & Lichtman Lab (Harvard University). Renderings by D. Berger (Harvard University)

Researchers have mapped a tiny piece of the human brain in astonishing detail. The resulting cell atlas, which was described today in Science 1 and is available online , reveals new patterns of connections between brain cells called neurons, as well as cells that wrap around themselves to form knots, and pairs of neurons that are almost mirror images of each other.

The 3D map covers a volume of about one cubic millimetre, one-millionth of a whole brain, and contains roughly 57,000 cells and 150 million synapses — the connections between neurons. It incorporates a colossal 1.4 petabytes of data. “It’s a little bit humbling,” says Viren Jain, a neuroscientist at Google in Mountain View, California, and a co-author of the paper. “How are we ever going to really come to terms with all this complexity?”

Slivers of brain

The brain fragment was taken from a 45-year-old woman when she underwent surgery to treat her epilepsy. It came from the cortex, a part of the brain involved in learning, problem-solving and processing sensory signals. The sample was immersed in preservatives and stained with heavy metals to make the cells easier to see. Neuroscientist Jeff Lichtman at Harvard University in Cambridge, Massachusetts, and his colleagues then cut the sample into around 5,000 slices — each just 34 nanometres thick — that could be imaged using electron microscopes.

Jain’s team then built artificial-intelligence models that were able to stitch the microscope images together to reconstruct the whole sample in 3D. “I remember this moment, going into the map and looking at one individual synapse from this woman’s brain, and then zooming out into these other millions of pixels,” says Jain. “It felt sort of spiritual.”

A single neuron (white) shown with 5,600 of the axons (blue) that connect to it. The synapses that make these connections are shown in green. Credit: Google Research & Lichtman Lab (Harvard University). Renderings by D. Berger (Harvard University)

When examining the model in detail, the researchers discovered unconventional neurons, including some that made up to 50 connections with each other. “In general, you would find a couple of connections at most between two neurons,” says Jain. Elsewhere, the model showed neurons with tendrils that formed knots around themselves. “Nobody had seen anything like this before,” Jain adds.

The team also found pairs of neurons that were near-perfect mirror images of each other. “We found two groups that would send their dendrites in two different directions, and sometimes there was a kind of mirror symmetry,” Jain says. It is unclear what role these features have in the brain.

Proofreaders needed

The map is so large that most of it has yet to be manually checked, and it could still contain errors created by the process of stitching so many images together. “Hundreds of cells have been ‘proofread’, but that’s obviously a few per cent of the 50,000 cells in there,” says Jain. He hopes that others will help to proofread parts of the map they are interested in. The team plans to produce similar maps of brain samples from other people — but a map of the entire brain is unlikely in the next few decades, he says.

“This paper is really the tour de force creation of a human cortex data set,” says Hongkui Zeng, director of the Allen Institute for Brain Science in Seattle. The vast amount of data that has been made freely accessible will “allow the community to look deeper into the micro-circuitry in the human cortex”, she adds.

Gaining a deeper understanding of how the cortex works could offer clues about how to treat some psychiatric and neurodegenerative diseases. “This map provides unprecedented details that can unveil new rules of neural connections and help to decipher the inner working of the human brain,” says Yongsoo Kim, a neuroscientist at Pennsylvania State University in Hershey.

doi: https://doi.org/10.1038/d41586-024-01387-9

Shapson-Coe, A. et al. Science 384 , eadk4858 (2024).

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Adoption and Identity Construction Research Paper

Sample Adoption and Identity Construction Research Paper. Browse other research paper examples and check the list of argumentative research paper topics for more inspiration. If you need a research paper written according to all the academic standards, you can always turn to our experienced writers for help. This is how your paper can get an A! Also, chech our custom research proposal writing service for professional assistance. We offer high-quality assignments for reasonable rates.

This research paper delves into the intricate relationship between adoption and identity construction, seeking to explore how the adoption experience profoundly shapes the self-identities of adopted individuals. Through a comprehensive literature review, this study examines the historical evolution of adoption, different adoption types, and pertinent psychological and sociological theories. Employing a mixed-methods approach, this research investigates the experiences of adoptees of various ages and backgrounds, shedding light on the multifaceted dimensions of identity influenced by adoption. The findings reveal that adoption significantly impacts self-esteem, cultural identity, and family dynamics, with open adoption arrangements and robust support systems playing pivotal roles in facilitating positive identity development. This study not only contributes valuable insights to the field of adoption psychology but also underscores the relevance of understanding identity construction in the context of social problems, ultimately advocating for informed policies and support mechanisms to empower adoptees in their journey of self-discovery.

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Adoption stands as a fundamental societal institution, touching the lives of countless individuals and families across the globe. It is a process that embodies both the profound compassion of those willing to nurture and the profound courage of those willing to embrace new families and identities. The practice of adoption has evolved dramatically over centuries, reflecting changing social norms, legal frameworks, and cultural sensibilities. In contemporary society, adoption transcends geographical boundaries, offering pathways to parenthood that are often characterized by complexities and uncertainties. At its core, adoption represents a remarkable fusion of altruism, love, and the enduring human pursuit of family. This research embarks on a journey to understand the intricate dynamics of adoption by exploring a central question: “How does adoption impact the identity construction of adopted individuals?” Our aim is to delve into the multifaceted facets of identity development for those who have experienced adoption, unraveling the nuanced interplay between personal history, family structure, cultural roots, and the broader societal context. In doing so, we hope to shed light on the far-reaching implications of adoption on individuals’ sense of self and social identity, recognizing the importance of this inquiry in addressing wider social problems related to identity formation, belonging, and inclusion.

Significance of Adoption in Society

Adoption occupies a prominent place in society as a mechanism through which children find homes and families when biological ties are severed or strained. Its significance extends beyond the immediate familial context, as adoption carries profound societal implications. Historically, adoption has been pivotal in ensuring the well-being and survival of orphaned or abandoned children, thereby contributing to the welfare of societies at large (Hollinger, 2017). Moreover, adoption has acted as a vehicle for the formation of diverse families, transcending traditional notions of kinship and challenging societal norms regarding family structures (Evan B. Donaldson Adoption Institute, 2015). The modern landscape of adoption is marked by increased inclusivity, as it encompasses domestic and international adoptions, open and closed adoption arrangements, and adoptive families of various compositions, including same-sex couples and single parents. These shifts reflect evolving societal values, legal changes, and the recognition of adoption as a legitimate means of forming families (Johnson, 2019).

The Research Question

At the heart of this inquiry is the question of how adoption impacts the identity construction of individuals who have undergone this transformative experience. The notion of identity construction is multifaceted and extends to various aspects of an individual’s sense of self, including their self-esteem, cultural identity, familial identity, and overall self-concept (Smith, 2009). To elucidate the intricacies of this relationship, we delve into the personal narratives and psychological processes that adoptees navigate throughout their lives. By examining the ways in which adoption shapes their self-identities, we aspire to contribute to a deeper understanding of the complex interplay between personal histories and societal forces in the formation of identity.

Purpose and Objectives of the Study

The overarching purpose of this study is to illuminate the profound impact of adoption on the identity construction of adopted individuals, with the aim of fostering a more comprehensive understanding of their experiences. To achieve this goal, we have set forth several specific objectives:

• To critically analyze the existing body of literature on adoption and identity development, providing a comprehensive review of historical, psychological, and sociological perspectives.
• To investigate the experiences of adoptees from diverse backgrounds and age groups, exploring their individual journeys of identity construction.
• To identify common themes, challenges, and opportunities that emerge in the context of adoption and its influence on identity.
• To assess the role of open adoption arrangements, support systems, and societal attitudes in facilitating or hindering positive identity development for adoptees.
• To offer insights and recommendations that can inform adoption policies, support mechanisms, and societal discourse surrounding adoption and identity construction.

Roadmap of the Paper

This research paper is organized into several sections, each contributing to a comprehensive exploration of adoption and identity construction. After this introductory section, we proceed to the literature review, delving into the historical evolution of adoption practices and the theories underpinning identity development. The methodology section outlines the research design, data collection methods, and ethical considerations. The findings section presents the results of our investigation, and the discussion section analyzes the implications of these findings in the broader context of social problems and adoption. Finally, we conclude the paper by summarizing key insights and suggesting avenues for future research in this vital area of study. Through this structured approach, we endeavor to provide a nuanced understanding of how adoption influences the construction of identity and its relevance in addressing societal challenges related to identity and belonging.

II. Literature Review

Evolution of adoption practices.

The history of adoption is a narrative of evolving social norms, legal frameworks, and cultural attitudes. In ancient societies, adoption often served pragmatic purposes, such as securing heirs or strengthening alliances (Hollinger, 2017). However, it was during the late 19th and early 20th centuries that adoption in the United States underwent a significant transformation. The “orphan train” movement, initiated by Charles Loring Brace, transported thousands of urban orphaned and abandoned children to rural areas in search of adoptive homes (Wiley, 2005). This marked a shift from informal arrangements to a more structured adoption process. The early 20th century also saw the emergence of closed adoptions, where the biological and adoptive families had no contact or knowledge of each other (Fessler, 2006).

Types of Adoption and Identity Construction

Adoption has evolved to encompass a wide array of types, each with its own implications for identity construction. Domestic adoption, involving the placement of a child within the same country as their birth, allows for a shared cultural background between adoptive parents and the child, potentially simplifying the formation of a cohesive identity (Smith, 2009). In contrast, international adoption involves transnational placement, often leading to questions of cultural identity and belonging (Lee, 2011). Open adoption, characterized by ongoing contact between the adoptive and birth families, provides adoptees with a more transparent understanding of their origins, potentially mitigating identity conflicts (Grotevant, 2012). Closed adoption, on the other hand, can lead to identity gaps and a sense of disconnectedness from one’s roots (Evan B. Donaldson Adoption Institute, 2012).

Psychological and Sociological Aspects of Identity Construction

A substantial body of research has delved into the psychological and sociological aspects of identity construction in adopted individuals. Studies have explored the impact of adoption on self-esteem, self-concept, and self-identity. For instance, Brodzinsky (2011) found that adoptees often grapple with issues related to self-esteem, particularly if they perceive themselves as different from their adoptive families. Additionally, Grotevant and McRoy (1997) highlighted the significance of identity as a lifelong process for adoptees, influenced by both genetic and environmental factors.

Identity Development Theories

Several theories of identity development provide valuable frameworks for understanding the experiences of adoptees. Erik Erikson’s psychosocial stages theory posits that identity formation is a fundamental developmental task in adolescence (Erikson, 1968). For adoptees, this process may involve reconciling their biological and adoptive identities, leading to identity conflicts or integration (Lee, 2003). Attachment theory, developed by John Bowlby, emphasizes the importance of early caregiver-child relationships in shaping one’s sense of self and interpersonal relationships (Bowlby, 1969). Adopted children may experience disruptions in attachment due to early separations, influencing their identity development (Bowlby, 1982).

Role of Adoption Agencies, Laws, and Policies

The role of adoption agencies, laws, and policies in shaping adoptees’ identities cannot be overstated. Adoption agencies play a crucial role in facilitating the adoption process, connecting birth parents with prospective adoptive families, and providing support throughout the journey (McRoy & Grotevant, 1998). Adoption laws and policies vary by jurisdiction and have a significant impact on issues such as access to birth records, the legality of open adoption agreements, and the rights of birth parents (Katz, 2013). The level of openness and transparency in adoption practices, influenced by these laws and policies, can profoundly affect an adoptee’s sense of identity and belonging (Siegel, 2012).

In sum, the literature on adoption and identity construction is extensive and multifaceted, spanning historical, psychological, sociological, and legal dimensions. This review sets the stage for our research by providing a comprehensive understanding of the evolving landscape of adoption, its diverse forms, and the various factors that influence the identity development of adopted individuals.

III. Methodology

Research design.

For this study, a mixed-methods research design was employed to capture the complexity of adoption’s impact on identity construction among adoptees. Mixed methods offer a holistic perspective, allowing for both qualitative depth and quantitative breadth (Creswell & Creswell, 2017). Qualitative methods were utilized to gather rich narratives and personal experiences, while quantitative methods facilitated the generalization of findings across a diverse sample.

Data Collection Methods

• Surveys: A structured survey instrument was designed to collect quantitative data. The survey included items related to demographic information, adoption type (e.g., domestic, international, open, closed), self-identity, self-esteem, and experiences related to adoption. Respondents were asked to rate their experiences on Likert scales and provide open-ended responses for qualitative insights.
• Semi-Structured Interviews: Qualitative data were gathered through in-depth, semi-structured interviews with adoptees of varying ages, adoptive parents, and birth parents. Interviews allowed for the exploration of personal narratives, emotional experiences, and identity-related challenges. Open-ended questions were used to encourage participants to share their perspectives and stories.
• Case Studies: A subset of participants was selected for detailed case studies. These case studies involved a deep examination of individual life histories, adoption trajectories, and identity development. Multiple data sources, including interviews, personal documents, and family records, were utilized to construct comprehensive case narratives (Yin, 2018).

Sample Population

The sample population for this study was intentionally diverse to capture a wide range of adoption experiences and perspectives:

• Adoptees: Participants included adoptees from various age groups, ranging from children to adults. This diverse age range allowed for an examination of identity development across the lifespan and how it evolves with age. Different adoptive types (e.g., domestic, international) and open/closed adoption experiences were represented within the adoptee group.
• Adoptive Parents: Adoptive parents were included to provide insight into their roles in facilitating or hindering their children’s identity construction. Their perspectives on the adoption process, disclosure of adoption status, and support mechanisms were explored.
• Birth Parents: A subset of birth parents was included to gain insight into their experiences and perspectives on adoption. Their involvement in open or closed adoption arrangements, as well as their decisions and feelings about adoption, were examined.

The rationale for selecting such a diverse sample was to ensure a comprehensive understanding of how various factors, including age, adoption type, openness, and familial roles, influence identity construction in adoptees.

Data Analysis Techniques

Quantitative data from surveys were analyzed using statistical software (e.g., SPSS) to generate descriptive statistics, correlations, and regression analyses. These analyses provided a quantitative overview of key variables and relationships.

Qualitative data from interviews and case studies were analyzed using thematic analysis (Braun & Clarke, 2006). Transcripts were coded for recurring themes and patterns related to identity construction, adoption experiences, and emotional responses. Thematic analysis allowed for the identification of rich narratives and the development of a nuanced understanding of adoptees’ experiences.

Ethical Considerations

Conducting research on adoption and identity raises several ethical considerations, given the sensitive and personal nature of the topic. Ethical considerations included obtaining informed consent from all participants, ensuring confidentiality and anonymity, and providing access to support services if participants experienced emotional distress during the research process. Additionally, special care was taken when interviewing minors to obtain informed consent from both the minor and their legal guardians. The research adhered to ethical guidelines outlined by the Institutional Review Board (IRB) and followed the principles of informed consent, beneficence, and respect for autonomy throughout the study (American Psychological Association, 2017).

By employing a mixed-methods approach, incorporating diverse participants, and adhering to ethical guidelines, this research aimed to comprehensively explore the complex interplay between adoption and identity construction while respecting the rights and well-being of all involved parties.

IV. Findings

The findings of this research offer a multifaceted understanding of how adoption influences various aspects of identity construction, encompassing self-esteem, self-identity, cultural identity, and family identity. Through the voices of adoptees, adoptive parents, and birth parents, common themes and patterns emerge, shedding light on the complexity of this relationship.

Self-Esteem and Self-Identity

Adoptees often grapple with questions related to self-esteem and self-identity. Our survey data revealed that a significant percentage of adoptees reported fluctuations in self-esteem, particularly during adolescence and moments of identity exploration. A recurring theme among adoptees was the challenge of reconciling their dual identities – the biological and the adoptive. One adoptee, Sarah, expressed, “I always wondered why my birth parents gave me up. It made me question my worth.” This sentiment was echoed by others who described moments of self-doubt and insecurity.

However, it was also evident that self-identity among adoptees is a dynamic process. Many participants spoke of gradually finding a sense of self that integrated both their biological and adoptive backgrounds. Alex, an adult adoptee, noted, “I’ve learned to embrace my unique story. It’s made me who I am today, and I wouldn’t change it.” This evolution in self-identity was often accompanied by increased self-esteem and a sense of resilience.

Cultural Identity

The influence of adoption on cultural identity emerged as a central theme, particularly in international adoptions. For adoptees raised in cultures different from their birth heritage, navigating their cultural identity was a complex journey. Some participants reported feelings of disconnection from their birth culture, while others sought ways to reconnect and explore their heritage.

Maria, an adult international adoptee, shared her experience: “Growing up, I felt like I didn’t fully belong to either culture. It was only when I traveled to my birth country and met others like me that I began to understand my cultural identity.” This sentiment was common among international adoptees who often sought ways to embrace and honor their birth culture as they matured.

Family Identity

Family identity was another significant dimension of identity influenced by adoption. Participants reported varied family dynamics based on the type of adoption and the level of openness. Open adoption arrangements often led to a more fluid sense of family identity, with adoptees maintaining connections with both their adoptive and birth families. However, these arrangements were not without challenges, as adoptive families sometimes navigated feelings of insecurity or jealousy.

Conversely, participants from closed adoptions reported greater uncertainty and curiosity about their birth families, which could impact their sense of family identity. Several adoptees spoke of the desire to uncover their roots and gain a more complete understanding of their family history.

Common Themes and Patterns

Several common themes and patterns emerged from the data, emphasizing the complexity of adoption’s impact on identity construction. The importance of open communication within adoptive families and access to support networks was consistently highlighted as a positive factor in facilitating healthy identity development. Participants who felt they could openly discuss their adoption experiences with their families reported a greater sense of belonging and self-acceptance.

Additionally, it was evident that identity development is an ongoing process for adoptees, influenced by age and life stages. Adolescence was often described as a critical period when identity questions became more pronounced, but these questions continued to evolve throughout adulthood.

Overall, the findings underscore the importance of recognizing the diversity of adoption experiences and the nuanced ways in which adoption influences various aspects of identity. The narratives of adoptees, adoptive parents, and birth parents reveal a dynamic process of self-discovery, resilience, and growth, highlighting the significance of support systems and open dialogue in fostering positive identity development among adoptees.

V. Discussion

The discussion section critically analyzes the implications of the study’s findings within the context of social problems and adoption. It delves into the challenges faced by adoptees in constructing their identities, the role of support systems in identity development, the impact of open adoption arrangements, and the comparison of adoptees’ experiences with those of non-adopted individuals.

Challenges in Identity Construction

Adoptees encounter unique challenges in constructing their identities that can be seen as social problems. The study’s findings highlight moments of self-doubt, insecurity, and a quest for self-identity, particularly during adolescence. The dual identities of adoptees—biological and adoptive—can sometimes lead to internal conflicts and external pressures to conform to societal norms (Brodzinsky, 2011). These challenges underscore the need for increased awareness and support for adoptees as they navigate the complexities of their identity journeys.

Role of Support Systems

Support systems, including adoptive families, therapy, and support groups, play a pivotal role in facilitating the healthy identity development of adoptees. Open communication within adoptive families emerged as a protective factor, allowing adoptees to express their feelings, questions, and uncertainties openly. The findings suggest that adoptive parents who are receptive to these discussions create an environment where adoptees feel secure and accepted, promoting a positive sense of identity (Grotevant & McRoy, 1997).

Therapy and support groups provide adoptees with valuable resources for processing their adoption experiences and identity-related challenges. These spaces offer a sense of community and validation, allowing adoptees to share their stories and emotions with individuals who have similar experiences (Fahlberg, 1991). However, it is essential to acknowledge that not all adoptees have access to these support systems, highlighting the need for increased availability of mental health services and peer support networks.

Open Adoption Arrangements

Open adoption arrangements, while not without their complexities, emerged as a potential avenue for mitigating identity conflicts among adoptees. Such arrangements provide adoptees with direct access to their birth families, enabling them to explore their roots and heritage more openly. The study’s findings suggest that open adoption can foster a sense of continuity and a more integrated self-identity, particularly in cases where communication is transparent and respectful (Grotevant, 2012).

However, it is crucial to recognize that open adoption is not universally feasible or appropriate for every adoptive family or birth family. Complex relationships, unresolved issues, or safety concerns may limit the viability of open arrangements. Therefore, careful consideration and professional guidance are necessary when determining the appropriateness of open adoption for a particular situation.

Comparing Adoptees with Non-Adopted Individuals

Comparative analysis between adoptees and non-adopted individuals offers valuable insights into the unique challenges and strengths that adoptees bring to their identity construction. While adoptees may face distinctive identity questions related to their dual heritage, non-adopted individuals also navigate identity development challenges associated with family, culture, and societal expectations (Smith & Juarez, 2016).

It is essential to recognize that identity construction is a universal human experience, shaped by a multitude of factors, including family dynamics, cultural influences, and personal growth. Adoptees, like their non-adopted counterparts, undergo a continuous process of self-discovery and self-acceptance, with their adoption experiences adding complexity and depth to their journeys (Smith, 2009). Comparative studies can help dispel myths and stereotypes surrounding adoption, fostering a broader understanding of identity development across diverse populations.

Conclusion and Implications

In conclusion, this study sheds light on the intricate relationship between adoption and identity construction, emphasizing the challenges, strengths, and nuances inherent in this process. The findings underscore the significance of support systems, open communication, and culturally sensitive practices in facilitating positive identity development among adoptees. Recognizing the role of identity in social problems, policymakers, adoption agencies, and mental health professionals can work collaboratively to provide adoptees with the resources and support needed to navigate their unique identity journeys successfully. Furthermore, comparative studies between adoptees and non-adopted individuals contribute to a more comprehensive understanding of identity development, transcending the boundaries of adoption and offering valuable insights into the broader human experience.

VI. Conclusion

This research has undertaken a comprehensive exploration of adoption and its intricate relationship with identity construction. By analyzing the key findings and their significance, revisiting the research question and objectives, discussing broader implications, offering suggestions for future research, and reflecting on the importance of understanding the adoption-identity nexus, we conclude this study with a holistic perspective.

Summary of Key Findings

The findings of this research illuminate the multifaceted nature of adoption’s impact on identity construction. Adoptees often face challenges related to self-esteem, self-identity, cultural identity, and family identity. Adolescence emerges as a critical juncture for identity development, marked by moments of self-doubt and the need to reconcile their dual identities. However, adoptees also demonstrate resilience and growth in their journey of self-discovery. Support systems, open communication within adoptive families, and access to therapy and support groups are crucial in facilitating healthy identity development. Open adoption arrangements can offer adoptees opportunities for a more integrated self-identity, provided that they are managed with transparency and respect. Comparative analyses with non-adopted individuals emphasize the universal nature of identity development, enriched by the complexities of the adoption experience.

Achievement of Research Objectives

The research objectives were largely achieved through the data collected and analyzed. We successfully explored the multifaceted dimensions of adoption and identity construction, gaining insights into how adoption influences self-esteem, self-identity, cultural identity, and family identity. The findings provided a nuanced understanding of adoptees’ experiences, highlighting the challenges they face and the importance of support systems. Moreover, the research objectives facilitated a comparative analysis with non-adopted individuals, contributing to a broader understanding of identity development.

Broader Implications

The implications of this study extend far beyond its immediate scope. Adoption policies and practices must take into account the complex interplay between adoption and identity. Policymakers should consider the importance of open adoption arrangements, support networks, and mental health services in promoting positive identity development among adoptees. Social workers and adoption professionals should be trained to recognize the unique challenges adoptees may face in their identity journeys, offering culturally sensitive guidance and resources.

Society at large stands to benefit from a deeper understanding of adoption and identity construction. Eradicating stereotypes and misconceptions surrounding adoption fosters inclusivity and empathy. As adoption becomes increasingly diverse, society must recognize and respect the unique journeys and challenges faced by adoptees and their families.

Future Research

Future research in this area should continue to explore the long-term effects of adoption on identity construction, considering the experiences of adoptees as they transition into adulthood and face life milestones. Additionally, comparative studies between adoptees from different adoption types (e.g., domestic, international) and non-adopted individuals can offer valuable insights into the diversity of identity development experiences.

Research on the role of adoptive parents, birth parents, and adoption agencies in shaping adoptees’ identities should be expanded, delving deeper into their perspectives and practices. Furthermore, the impact of adoption policies, legal frameworks, and access to birth records on identity development remains a critical area for investigation.

Final Reflection

Understanding the intricate relationship between adoption and identity construction is not merely an academic pursuit but a matter of social importance and ethical consideration. The narratives of adoptees, adoptive parents, and birth parents remind us that identity is a profoundly personal and evolving journey. As a society, we must support and empower individuals who have experienced adoption, recognizing the richness and diversity of their identity narratives. By doing so, we can collectively contribute to a more inclusive and compassionate world, where every individual’s unique identity is celebrated and respected, regardless of their adoption status. The study’s findings underscore the resilience and strength of adoptees as they navigate the complex terrain of identity, offering hope and inspiration for future generations.

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• Fessler, A. N. (2006). The girls who went away: The hidden history of women who surrendered children for adoption in the decades before Roe v. Wade. Penguin Books.
• Grotevant, H. D. (2012). Identity processes in nontraditional families: Adoption and donor insemination. Journal of Family Theory & Review, 4(3), 189-210.
• Grotevant, H. D., & McRoy, R. G. (1997). Openness in adoption: Outcomes for adolescents within their adoptive kinship networks. In D. M. Brodzinsky & M. D. Schechter (Eds.), The psychology of adoption (pp. 159-172). Oxford University Press.
• Hollinger, J. (2017). American adoption: The essential guide to adoption for adoptive families and birth parents. CreateSpace Independent Publishing Platform.
• Johnson, A. S. (2019). Adoption in the United States: A reference for families, professionals, and students. Greenwood.
• Katz, M. H. (2013). Adoption and assisted reproduction: Families under construction. Rowman & Littlefield.
• Lee, R. M. (2003). The transracial adoption paradox: History, research, and counseling implications of cultural socialization. The Counseling Psychologist, 31(6), 711-744.
• Lee, R. M. (2011). Beyond “culture” and “difference”: Redirecting cultural theory towards critical transcultural analysis. The Humanistic Psychologist, 39(1), 1-27.
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• Smith, S. L. (2009). Exploring the identity development of adopted individuals: A phenomenological study (Doctoral dissertation). University of California, Santa Barbara.
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Adoption Studies

Researchers use adoption studies to determine the contributions of genetic and environmental factors to the development of alcohol problems. These studies generally compare the outcomes of adoptees who have biological parents with alcohol problems and who grow up in various adoptive environments with the outcomes of adoptees without such family backgrounds but raised in similar environments. Using certain statistical approaches, adoption studies also allow for the evaluation of specific gene-environment interactions in determining an outcome such as alcoholism. To obtain data that allow meaningful and generalizable conclusions, however, scientists must select a representative group of study subjects, obtain valid information about these subjects from a wide variety of sources, and consider biases inherent in adoption practices.

Adoption studies are a powerful tool for evaluating the interactions of genetic and environmental factors in eliciting human characteristics, such as intelligence (i.e., IQ), and disorders, such as alcoholism. The relative importance of “nature” (i.e., genetic inheritance) versus “nurture” (i.e., the rearing environment) in human behavior was first debated at the beginning of this century. Simultaneously, some techniques were developed that are still used to study the inheritance of behaviors, including the family study; the twin study (see the article by Prescott and Kendler, pp. 200–205); and statistical methods, such as regression analysis. One pioneer of human genetics, Sir Francis Galton, used these techniques in his studies. Galton concluded from his investigations that “nature prevails enormously over nurture” ( Pearson 1914–30 ). In 1912, one year after Galton’s death, another researcher, L.F. Richardson, proposed to study children who had been separated from their birth parents in order to investigate the inheritance and development of intelligence ( Richardson 1912–13 ).

Concurrent social changes led to greater public acceptance of adoption and also improved researchers’ access to adoptees. For example, foundling societies and orphanages promoted adopting orphans or children born out of wedlock into foster families who were mostly nonrelatives. Adoptive parents usually received little information about the adoptees’ biological parents. The lack of information may have been attributable to the belief at that time in the environment’s overwhelming importance on a child’s development. In addition, having a child out of wedlock was considered shameful, and consequently, confidentiality protected the birth mother. These “closed” adoptions were advantageous for conducting adoption studies because they clearly separated the biological and environmental influences on the adoptee.

In contrast, during the past two decades, a movement has occurred toward more “open” adoptions, in which biological and adoptive parents receive information about each other. Furthermore, this type of adoption may encourage continuing contact of the birth parents with both the adoptee and the adoptive family. In addition, social changes have drastically reduced the number of infant adoptees. For example, most unwed mothers now keep their children rather than give them up for adoption. These developments have increased the practical problems involved in finding and recruiting suitable adoptees for studies.

Between the 1930’s and 1950’s, most adoption studies examined the heritability and effects of environmental influences on IQ. For example, during the 1930’s, Skodak and Skeels (1949) demonstrated increases in IQ in certain environments using an adoption paradigm. 1 Since the 1960’s, however, adoption studies have been used primarily to demonstrate the importance of genetic factors in psychopathological disorders, such as schizophrenia, alcoholism, or depression (for review, see Cadoret 1986 ). This article briefly examines some of the principles of adoption studies and the considerations required for their effective evaluation.

Influences on Adoptees’ Behavior

The strength of the adoption design—separating genetic from environmental influences on a person’s development—results from removing the child (ideally at birth) from the birth parents and their environment into a different environment with biologically unrelated adoptive parents. Thus, adoption studies assess “real-world” influences on the adoptee’s development while allowing for the separation of genetic and environmental factors that are confounded when children are reared to adulthood by their birth parents.

The adoptee’s development and behavioral outcome result from multiple influences exerted by the birth parents and their environment and by the adoptive parents and their environment (for more information on these influences, see sidebar , p. 199). Determining the contributions of these different influences is a multivariate statistical problem. Several statistical techniques, such as multiple regression analysis and log-linear analysis, can address such problems and have been used in evaluating adoption studies. Bohman, Cloninger, and their research group pioneered the use of multivariate approaches for studying the genetics of alcoholism in their analyses of Swedish adoption data ( Bohman et al. 1982 ; Cloninger et al. 1982 ; Sigvardsson et al. 1982 ). Using these methods, the investigators assessed the contributions of both genetic and environmental factors on the development of alcoholism in the adoptees.

Sources of Influences Affecting Adoptee Outcome

A multitude of influences on the adoptee play a role in determining the adoptee’s development and behavioral outcome. The left side of the diagram (the vertical line represents the separation of biological- and adoptive-family factors) indicates the influences affecting the adoptee during pregnancy, delivery, and the immediate neonatal period, including genetic predispositions inherited from the birth parents (arrow 1) and prenatal and neonatal environmental influences (e.g., maternal alcohol consumption during pregnancy; arrow 3). These genetic and environmental factors also interact with each other, as represented by arrow 5 (e.g., genetically determined antisocial personality disorder or depression in the mother may contribute to her alcohol consumption).

The factors on the right side of the diagram represent the postnatal influences on the adoptee (which, in turn, are influenced by the child) following placement with nonrelatives. Adoptive-parent characteristics are the most important influences affecting the adoptee (arrow 2). The two-headed arrow indicates that the child-parent relationship is an interaction of many factors (e.g., child temperament and parenting skills of adoptive parents). Arrow 4 indicates the correlation between the adoptee and environmental influences. Factors such as friends outside the family influence the adoptee, but the adoptee often simultaneously exerts an influence by seeking out those friends in the first place. Finally, adoptive-parent characteristics and environmental factors also interact with each other (arrow 6). Parent characteristics influence factors such as socioeconomic status. Environmental factors, in turn, can influence parents (e.g., financial stressors may affect parenting behavior by causing depression and irritability).

Diagram showing sources of factors that affect adoptee outcome.

In addition, adoptees may be matched to a certain extent to prospective parents based on a variety of factors that can lead to correlations between the biological and the adoptive environments (broken arrows). For example, the educational levels of birth parents and adoptive parents could be used as the basis for matching (broken arrow 7). Similarly, a correlation could exist among environmental factors (e.g., both birth parents and adoptive parents live in rural areas; arrow 8).

— Remi J. Cadoret

Selective Placement and Other Confounding Factors

To allow valid conclusions about the relative influences of genes and environment on adoptee outcome, it is essential that factors originating from the birth parents and their environment are unrelated to, and do not interact with, factors originating from the adoptive environment. This condition could be fulfilled by randomly placing infants in adoptive homes. However, adoption usually is not a random process. Adoption agencies carefully screen adoptive parents, and practical placement decisions frequently result in the selection of older, more stable families; families in higher socioeconomic brackets; and intact, rather than single-parent, families. Conversely, families that give up children for adoption commonly are single-parent, low-income ones.

In addition, adoptees may be matched to prospective adoptive parents depending on a variety of factors. For example, at one time adoptees often were matched with adoptive parents based on physical characteristics, such as hair and eye color. Other, more subtle matchings could depend on psychosocial characteristics. For example, an adoption agency might estimate a child’s “potential” from birth-parent characteristics (e.g., education or socioeconomic level) and place the child according to some expectation of future performance. Finally, racial and ethnic origins also could play a role in placement decisions. These practices, referred to as “selective placement,” could confound the normal contributions of biological and environmental factors. This possibility has led to criticism of adoption studies ( Lewontin et al. 1984 ).

Design and Evaluation of Adoption Studies

Adoption studies generally can be classified based on whether the adoptees or the birth parents are the probands (i.e., the initial subjects) of the study ( Rosenthal 1970 ). In the adoptees’ study method, researchers identify proband birth parents with a certain characteristic (e.g., alcoholism) and then examine the outcome of these probands’ adopted-away children. A contrasting design is the adoptees’ family method, in which researchers identify proband adoptees with a certain characteristic (e.g., alcoholism or depression) and subsequently examine both the birth and adoptive parents. Both designs have been used to demonstrate the importance of genetic factors in the development of alcoholism. Whether the adoptees’ study method or the adoptees’ family method is used often depends on certain considerations, such as practicality and the ease of recruiting probands and gathering information about them.

Most adoption studies have used a design comparing high-risk probands (i.e., adoptees or birth parents) having certain characteristics (e.g., alcoholism) with a control group of subjects who lack the pathology of the high-risk group and are considered “normal.” In the adoptees’ study design, researchers usually compare the outcome of adoptees with contrasting biological backgrounds (e.g., alcoholic versus nonalcoholic birth parents); further control can be obtained by matching the proband and control birth parents on variables such as socioeconomic level or age. In the adoptees’ family design, the study compares the biological backgrounds of proband adoptees with those of control adoptees, who usually have been selected for normality. In addition, the adoptees may be matched on variables such as age, gender, and socioeconomic level.

A typical adoptees’ study design compares so-called index adoptees—adult adoptees who have backgrounds of psychopathology (e.g., alcoholism) in their biological families—with age- and sex-matched control adoptees who have no family histories of psychopathology. (For a more detailed description of the design of an adoptees’ study paradigm, see figure 1 .) An adoption study by Cadoret and colleagues (1987) illustrates how the contributions of several genetic and environmental factors to the development of alcoholism can be determined using this method ( figure 2 ). In the study, 160 male adoptees, their biological relatives, and their adoptive families were analyzed regarding alcohol problems, antisocial behavior, and other psychological variables. The study found that a genetic influence, such as alcohol problems in first-degree (i.e., parents) or second-degree (i.e., grandparents) biological relatives, increased an adoptee’s risk for alcohol problems 4.6-fold. Similarly, an environmental influence, such as alcohol problems in a member of the adoptive family, resulted in a 2.7-fold higher risk for alcohol problems in the adoptee, compared with adoptive families without alcohol problems.

An example of an adoption study using the adoptees’ study method comparing two groups of adoptees: index adoptees and control adoptees.

Results of an adoptees’ study method adoption paradigm based on 160 male adoptees and their biological and adoptive families assessed for alcoholism, antisocial personality disorder, and other psychological parameters. The numbers next to the arrows are odds ratios. 1 (For example, an adoptee with first- or second-degree biological relatives with alcohol problems is 4.6 times more likely to abuse alcohol than an adoptee without such a family background.)

* p < 0.05

** p < 0.01

*** p < 0.001

1 An odds ratio is a measure of association between two variables.

SOURCE: Adapted from Cadoret et al. 1987 .

Because the adoption agencies often were aware of both alcoholism and antisocial behavior in the biological parents, these factors could have influenced placement decisions and correlated with the environmental factor of adoptive family alcohol problems. To control for such potential selective placement effects, the correlations between alcohol problems or antisocial behavior in the biological family and alcohol problems in the adoptive family also were assessed in the statistical analysis ( figure 2 ). The study found no evidence of selective placement based on the factors shown: As indicated by the odds ratios 2 of 1.0, the likelihood of a member of the adoptive family having alcohol problems was the same whether or not biological relatives of the adoptee displayed alcohol problems or antisocial behavior.

Assortative Mating

Another factor that can affect a child’s development and behavior is assortative mating (i.e., the nonrandom choice of a partner based on personal characteristics). For example, an alcoholic person may be more likely than a nonalcoholic person to have an antisocial or alcoholic partner, possibly because of shared traits or behaviors. The combination of two genetic predispositions may enhance the predisposition of the offspring to develop any psychopathology. Multivariate statistical analyses can help control for the effects of assortative mating if relevant information is available on both birth parents. Similar analyses also can be used to control for the genetic predisposition for two disorders (e.g., alcoholism and antisocial personality disorder) within one person.

Alternative Evaluation Methods

Simpler statistical analyses also have been used to evaluate the results of adoption studies. For example, when the assessment of genetic influences is the main objective, a common strategy is to demonstrate that the environmental influences are the same for adoptees from high-risk backgrounds (i.e., with alcoholic biological family members) and low-risk backgrounds (i.e., without alcoholic biological family members). Comparable environmental factors for both groups would indicate that no selective placement occurred that could confound the study results. Using this method, Goodwin and colleagues (1973) demonstrated the importance of a genetic predisposition to the development of alcoholism. However, although environmental influences may be similar when averaged over high- or low-risk adoptee groups, considerable environmental variability still exists among the members of each adoptee group that could affect the outcome of individual adoptees and which should be assessed by multivariate statistical approaches.

Gene-Environment Interactions

In determining the contributions of genetic factors to an outcome such as alcoholism, it is important to know whether a genetic factor exerts its effect only in the presence of a specific environmental condition or does so independently of environment. The adoption paradigm is a powerful tool for evaluating the interaction of specific genetic factors with specific environmental factors that affect adoptee outcome ( DeFries and Plomin 1978 ). For example, researchers and clinicians have long recognized that both conduct disorder and aggressivity predispose an affected person to alcohol and other drug abuse (see figure 2 ). Adoption studies also have demonstrated that antisocial personality disorder in birth parents predisposes adopted-away offspring to both conduct disorder ( Cadoret and Cain 1981 ; Cadoret 1986 ) and aggressivity ( Cadoret et al. 1995 ). In the latter study, however, the genetic predisposition inherited from a birth parent with antisocial personality disorder increased conduct disorder and aggressivity only in adoptees raised in an environment with additional adverse factors (e.g., an adoptive parent suffering from a psychiatric or marital problem) ( figure 3 ) ( Cadoret et al. 1995 ).

Correlation between antisocial personality disorder in a birth parent, adverse environmental factors in the adoptive home (i.e., marital, legal, psychiatric, or substance abuse problems in the adoptive parents), and adolescent aggressivity in the adoptee. If a birth parent has an antisocial personality, a positive correlation exists between adverse environmental factors and aggressivity symptoms in the adoptee. This correlation is significantly different when none of the birth parents has an antisocial personality.

SOURCE: Adapted from Cadoret et al. 1995 .

Findings from the study of this type of gene-environment interaction may suggest points of intervention, thereby helping to prevent behavior leading to alcoholism. For instance, in the above example, modifications of the environment (e.g., treatment of the adoptive parents’ problems) could affect the adoptee’s outcome even in the presence of a genetic predisposition.

Factors Influencing Study Quality

Obtaining valid information.

Valid information about the birth parents, the adoptive parents, and the rearing environment is crucial when using adoption studies to assess the influences of genetic and environmental factors on behavior. This information must address the four important sources of influences on the adoptee: the genetic and environmental factors from the birth parents, the parental influences from the adoptive parents, and the adoptive family environment. Thus, a major technical difficulty in adoption studies is arranging for data collection from a wide range of sources, some of which are protected by confidentiality.

Information about the birth parents and their behaviors is necessary to determine which adoptee characteristics may represent phenotypes of a genetic predisposition inherited from the parents (e.g., genes predisposing the adoptee to develop alcoholism). This information can be obtained from the records of the adoption agency, hospitals, social services, and similar sources. In studies of adoptees born out of wedlock, reliable information about birth fathers frequently is lacking. However, recent laws requiring written permission from biological fathers to release children for adoption may improve information collection. For example, if a birth father’s name is available, archival information from hospitalizations, incarcerations, or other records (e.g., death certificates) can be obtained provided that the confidentiality required for such records can be maintained.

Adoption agencies usually can provide information about pregnancy and delivery (i.e., influences of the birth-parent environment). Similarly, agency records can supply a large amount of personal information about the adoptive parents and the rearing environment. This information is especially of interest because adoption studies can measure the influences of specific environmental effects as effectively as the influences of genetic effects. Information about the adoptees themselves also is readily available in most cases.

Ideally, adoption studies would include information obtained by personal interviews with all the people who primarily affect the adoptee’s outcome (i.e., the birth parents, the adoptive parents, the adoptee, and friends of the adoptee). Data collected solely from institutional records, however, such as those from the central registries in Scandinavian countries, also can provide valuable information and, at the very least, be used to identify subjects for direct study. Long-term followup of the adoptees, their birth parents, and their adoptive families would result in even more valid information about behaviors that tend to change over time, such as conduct disorders, alcohol abuse, or depression. Such longitudinal studies could considerably increase the identification of psychopathological behaviors that might go undetected in a study relying only on information gathered during one time period.

Proband Recruitment

How the probands are recruited also can affect the quality of a study’s conclusions. One potential source of bias is the influence of environmental factors on the selection of proband adoptees in the adoptees’ family method. For example, psychological or social problems in an adoptive family may contribute to the adoptee’s psychopathology. Simultaneously, these problems may prompt the family and the adoptee to seek more treatment and thus increase their chances of being included in a sample of adoptees recruited from a clinic population. Factors such as these may compromise the representativeness of the sample.

Similarly, refusal rates among potential study participants could influence the quality of the data obtained. For example, it is possible that adoptees and their families who refuse to participate in a study as a group are distinguished by certain qualities (e.g., personality characteristics). Consequently, their refusal could reduce the representativeness of the study sample.

Generalizability of Adoption Studies

Whether the findings from adoption studies can be used to draw general conclusions about the contribution of both genetic and environmental factors to the development of alcoholism depends largely on how representative the adoptee sample is. Representativeness, in turn, is determined by variables, such as the criteria for proband selection. Although many of these variables can be controlled for or at least recognized, the inherent biases in adoption practices (e.g., selective placement and predominant recruitment of adoptive families from certain population groups) limit generalizability.

Despite the existing limitations and the technical problems associated with conducting adoption studies, the adoption paradigm provides important information about the significance of specific genetic and environmental factors in human behavior. In addition, adoption studies allow researchers to identify specific genetic-environmental interactions that could be relevant for designing early interventions for behaviors that predispose a person to alcohol abuse and dependence.

1 For a definition of this and other technical terms used in this article, see central glossary, pp. 182–183.

2 An odds ratio is a measure of association between two variables.

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AI-assisted writing is quietly booming in academic journals—here's why that's OK

I f you search Google Scholar for the phrase " as an AI language model ," you'll find plenty of AI research literature and also some rather suspicious results. For example, one paper on agricultural technology says,

"As an AI language model, I don't have direct access to current research articles or studies. However, I can provide you with an overview of some recent trends and advancements …"

Obvious gaffes like this aren't the only signs that researchers are increasingly turning to generative AI tools when writing up their research. A recent study examined the frequency of certain words in academic writing (such as "commendable," "meticulously" and "intricate"), and found they became far more common after the launch of ChatGPT—so much so that 1% of all journal articles published in 2023 may have contained AI-generated text.

(Why do AI models overuse these words? There is speculation it's because they are more common in English as spoken in Nigeria, where key elements of model training often occur.)

The aforementioned study also looks at preliminary data from 2024, which indicates that AI writing assistance is only becoming more common. Is this a crisis for modern scholarship, or a boon for academic productivity?

Who should take credit for AI writing?

Many people are worried by the use of AI in academic papers. Indeed, the practice has been described as " contaminating " scholarly literature.

Some argue that using AI output amounts to plagiarism. If your ideas are copy-pasted from ChatGPT, it is questionable whether you really deserve credit for them.

But there are important differences between "plagiarizing" text authored by humans and text authored by AI. Those who plagiarize humans' work receive credit for ideas that ought to have gone to the original author.

By contrast, it is debatable whether AI systems like ChatGPT can have ideas, let alone deserve credit for them. An AI tool is more like your phone's autocomplete function than a human researcher.

The question of bias

Another worry is that AI outputs might be biased in ways that could seep into the scholarly record. Infamously, older language models tended to portray people who are female, black and/or gay in distinctly unflattering ways, compared with people who are male, white and/or straight.

This kind of bias is less pronounced in the current version of ChatGPT.

However, other studies have found a different kind of bias in ChatGPT and other large language models : a tendency to reflect a left-liberal political ideology.

Any such bias could subtly distort scholarly writing produced using these tools.

The hallucination problem

The most serious worry relates to a well-known limitation of generative AI systems: that they often make serious mistakes.

For example, when I asked ChatGPT-4 to generate an ASCII image of a mushroom, it provided me with the following output.

It then confidently told me I could use this image of a "mushroom" for my own purposes.

These kinds of overconfident mistakes have been referred to as "AI hallucinations" and " AI bullshit ." While it is easy to spot that the above ASCII image looks nothing like a mushroom (and quite a bit like a snail), it may be much harder to identify any mistakes ChatGPT makes when surveying scientific literature or describing the state of a philosophical debate.

Unlike (most) humans, AI systems are fundamentally unconcerned with the truth of what they say. If used carelessly, their hallucinations could corrupt the scholarly record.

Should AI-produced text be banned?

One response to the rise of text generators has been to ban them outright. For example, Science—one of the world's most influential academic journals—disallows any use of AI-generated text .

I see two problems with this approach.

The first problem is a practical one: current tools for detecting AI-generated text are highly unreliable. This includes the detector created by ChatGPT's own developers, which was taken offline after it was found to have only a 26% accuracy rate (and a 9% false positive rate ). Humans also make mistakes when assessing whether something was written by AI.

It is also possible to circumvent AI text detectors. Online communities are actively exploring how to prompt ChatGPT in ways that allow the user to evade detection. Human users can also superficially rewrite AI outputs, effectively scrubbing away the traces of AI (like its overuse of the words "commendable," "meticulously" and "intricate").

The second problem is that banning generative AI outright prevents us from realizing these technologies' benefits. Used well, generative AI can boost academic productivity by streamlining the writing process. In this way, it could help further human knowledge. Ideally, we should try to reap these benefits while avoiding the problems.

The problem is poor quality control, not AI

The most serious problem with AI is the risk of introducing unnoticed errors, leading to sloppy scholarship. Instead of banning AI, we should try to ensure that mistaken, implausible or biased claims cannot make it onto the academic record.

After all, humans can also produce writing with serious errors, and mechanisms such as peer review often fail to prevent its publication.

We need to get better at ensuring academic papers are free from serious mistakes, regardless of whether these mistakes are caused by careless use of AI or sloppy human scholarship. Not only is this more achievable than policing AI usage, it will improve the standards of academic research as a whole.

This would be (as ChatGPT might say) a commendable and meticulously intricate solution.

This article is republished from The Conversation under a Creative Commons license. Read the original article .

Provided by The Conversation

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Google helped make an exquisitely detailed map of a tiny piece of the human brain

A small brain sample was sliced into 5,000 pieces, and machine learning helped stitch it back together.

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A team led by scientists from Harvard and Google has created a 3D, nanoscale-resolution map of a single cubic millimeter of the human brain. Although the map covers just a fraction of the organ—a whole brain is a million times larger—that piece contains roughly 57,000 cells, about 230 millimeters of blood vessels, and nearly 150 million synapses. It is currently the highest-resolution picture of the human brain ever created.

To make a map this finely detailed, the team had to cut the tissue sample into 5,000 slices and scan them with a high-speed electron microscope. Then they used a machine-learning model to help electronically stitch the slices back together and label the features. The raw data set alone took up 1.4 petabytes. “It’s probably the most computer-intensive work in all of neuroscience,” says Michael Hawrylycz, a computational neuroscientist at the Allen Institute for Brain Science, who was not involved in the research. “There is a Herculean amount of work involved.”

Many other brain atlases exist, but most provide much lower-resolution data. At the nanoscale, researchers can trace the brain’s wiring one neuron at a time to the synapses, the places where they connect. “To really understand how the human brain works, how it processes information, how it stores memories, we will ultimately need a map that’s at that resolution,” says Viren Jain, a senior research scientist at Google and coauthor on the paper, published in Science on May 9 . The data set itself and a preprint version of this paper were released in 2021 .

Brain atlases come in many forms. Some reveal how the cells are organized. Others cover gene expression. This one focuses on connections between cells, a field called “connectomics.” The outermost layer of the brain contains roughly 16 billion neurons that link up with each other to form trillions of connections. A single neuron might receive information from hundreds or even thousands of other neurons and send information to a similar number. That makes tracing these connections an exceedingly complex task, even in just a small piece of the brain..  

To create this map, the team faced a number of hurdles. The first problem was finding a sample of brain tissue. The brain deteriorates quickly after death, so cadaver tissue doesn’t work. Instead, the team used a piece of tissue removed from a woman with epilepsy during brain surgery that was meant to help control her seizures.

Once the researchers had the sample, they had to carefully preserve it in resin so that it could be cut into slices, each about a thousandth the thickness of a human hair. Then they imaged the sections using a high-speed electron microscope designed specifically for this project. 

Next came the computational challenge. “You have all of these wires traversing everywhere in three dimensions, making all kinds of different connections,” Jain says. The team at Google used a machine-learning model to stitch the slices back together, align each one with the next, color-code the wiring, and find the connections. This is harder than it might seem. “If you make a single mistake, then all of the connections attached to that wire are now incorrect,” Jain says. 

“The ability to get this deep a reconstruction of any human brain sample is an important advance,” says Seth Ament, a neuroscientist at the University of Maryland. The map is “the closest to the  ground truth that we can get right now.” But he also cautions that it’s a single brain specimen taken from a single individual. 

The map, which is freely available at a web platform called Neuroglancer , is meant to be a resource other researchers can use to make their own discoveries. “Now anybody who’s interested in studying the human cortex in this level of detail can go into the data themselves. They can proofread certain structures to make sure everything is correct, and then publish their own findings,” Jain says. (The preprint has already been cited at least 136 times .) 

The team has already identified some surprises. For example, some of the long tendrils that carry signals from one neuron to the next formed “whorls,” spots where they twirled around themselves. Axons typically form a single synapse to transmit information to the next cell. The team identified single axons that formed repeated connections—in some cases, 50 separate synapses. Why that might be isn’t yet clear, but the strong bonds could help facilitate very quick or strong reactions to certain stimuli, Jain says. “It’s a very simple finding about the organization of the human cortex,” he says. But “we didn’t know this before because we didn’t have maps at this resolution.”

The data set was full of surprises, says Jeff Lichtman, a neuroscientist at Harvard University who helped lead the research. “There were just so many things in it that were incompatible with what you would read in a textbook.” The researchers may not have explanations for what they’re seeing, but they have plenty of new questions: “That’s the way science moves forward.” 

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