body image research paper questions

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Body Image Research

Body image: body image research, start learning about your topic.

It's important to begin your research learning something about your subject; in fact, you won't be able to create a focused, manageable thesis unless you already know something about your topic.

Useful Search Terms

Use the words below to search for useful information in books and articles.

• beauty culture

Depending on the focus your research takes, some of these terms may also help:

• eating disorders
• cosmetic surgery
• body dysmorphic disorder
• muscle dysmorphia

Use the Databases Below to Begin Learning About Your Topic

All of these resources are free for MJC students, faculty, & staff. 

• Gale eBooks This link opens in a new window Use this database for preliminary reading as you start your research. You'll learn about your topic by reading authoritative topic overviews on a wide variety of subjects.
• Films on Demand This link opens in a new window Type your search term into the search box in our film database to find short video clips suitable for academic research. more... less... Instructions for embedding Films on Demand into Canvas .

Create Research Questions to Focus Your Topic

Body image is a complex topic that is best dealt with by narrowing your focus. Do some background reading, and use the questions below to help you focus your topic. Once you have a more narrow topic, develop specific research questions (for an example, see the research questions on Body Image in our Developing Research Questions  guide).

• What is body image?
• How is one's body image developed? What is the role of family, community, and the media in creating body image?
• How do body image issues differ among men, women, teens and children? 
• How do diet, nutrition, or exercise affect body image?
• What is the connection between body image and cosmetic surgery?
• What is the connection between body image and eating disorders?
• What steps can be taken to improve one's body image?
• Based on what I have learned from my research what do I think about the issue of body image?

Explore Your Topic Further

When you are ready to explore and answer your research questions, use these databases below.

• Gale Databases This link opens in a new window Search over 35 databases simultaneously that cover almost any topic you need to research at MJC. Gale databases include articles previously published in journals, magazines, newspapers, books, and other media outlets.
• EBSCOhost Databases This link opens in a new window Search 22 databases simultaneously that cover almost any topic you need to research at MJC. EBSCO databases include articles previously published in journals, magazines, newspapers, books, and other media outlets.
• Explora This link opens in a new window Explora is a visual interface for accessing many EBSCO resources. It makes it easy for you to search by keyword or by topic to find the most useful search results. Results can be easily sorted by source type—magazines, reference books, photos, flags, etc.

Use the Web for Research

Search the web.

Use Google Scholar to find academically-appropriate Web sites.

Selected Websites

• Body Image / Our Bodies Ourselves Excerpts from the 2011 edition of Our Bodies Ourselves and links to relevant articles and sites from the Boston Women's Health Book Collective.
• Body Image / Psychology Today A collection of articles on different aspects of the body image issue from the popular magazine Psychology Today.
• Body Image / womenshealth.gov A federal government website managed by the Office on Women's Health at the U.S. Department of Health and Human Services.
• AboutFace.org A non-profit group advocating for reducing the media's influence on women's body image and self esteem.

Selected Library eBooks

Cite Your Sources

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Email: [email protected]

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Text: (209) 710-5270

• Last Updated: Apr 25, 2024 1:28 PM
• URL: https://libguides.mjc.edu/bodyimage

Except where otherwise noted, this work is licensed under CC BY-SA 4.0 and CC BY-NC 4.0 Licenses .

• Research article
• Open access

Journal of Eating Disorders

10k Accesses

8 Citations

1 Altmetric

Metrics details

ISSN: 2050-2974

• General enquiries: [email protected]

Social-media break has huge impact on young women's body image, study finds

Psychology prof says online exposure to idealized images creates 'infinite' opportunities for comparison.

There's a large and growing body of evidence pointing to potentially negative impacts of social media on mental health, from its addictive nature to disruptions in sleep patterns to effects on body image. Now, a new study coming out of York University's Faculty of Health found young women who took a social media break for as little as one week had a significant boost in self-esteem and body image -- particularly those most vulnerable to thin-ideal internalization.

"The statistician inside me was excited -- we don't often see effect sizes this large in my area of psychology research because human behaviour is complicated and there's lots of variability," says Psychology Professor Jennifer Mills, co-author of the paper. "We hope this study can be used to help protect young people and influence social media companies to give users more agency in how they interact with these platforms."

The paper, out this week in the journal Body Image , is thought to be the first to look specifically at social-media breaks and body image. Mills, whose lab has been on the forefront of this kind of research, collaborated on the paper with graduate researcher Lindsay Samson and undergraduate Olivia Smith, both students at York. They expected that there might be recruitment challenges, but it turns out there was enthusiasm for taking a social media pause among the 66 first-year female undergraduates who participated. Half were instructed to continue their social media as per usual, while the other half were given instructions to refrain from Instagram, Facebook, Twitter, and TikTok and other social media use for one week. They conducted baseline surveys before the experiment, and tested the participants again after the week was over.

"There's natural variability in how people feel about their bodies and about themselves in general, so we took that into account statistically, and even after that there were still significant differences between the groups after one week," says Mills, who is also the director of clinical training for the graduate psychology program.

The differences in the social media landscape are remarkable compared to when Mills started researching eating disorders and the effects of media, like magazines aimed at women.

"Back then, you could only spend so many minutes or hours looking at fashion and beauty magazines and they only came out once a month. There was a finite amount of content that you would be exposed to. With social media it's infinite. It's always new and novel, which triggers our brain's reward system that makes us want more and more of something."

Mills says the improvements found in this study might be explained both by women spending far less time engaging in behaviours known to have a detrimental effect, such as comparisons with others, but they may have also replaced social media with healthier behaviours.

"If we're spending more time in real life, socializing with friends, getting sleep, getting outdoors, getting exercise, there could be secondary behaviours that fill the void left by social media. Future research will try to disentangle that."

• Social Psychology
• Relationships
• Learning Disorders
• Popular Culture
• Media and Entertainment
• Surveillance
• Social Issues
• Philosophy of mind
• Public health
• Circadian rhythm sleep disorder
• Sociobiology
• Environmental impact assessment
• Social psychology

Story Source:

Materials provided by York University . Note: Content may be edited for style and length.

Journal Reference :

• Olivia E. Smith, Jennifer S. Mills, Lindsay Samson. Out of the loop: Taking a one-week break from social media leads to better self-esteem and body image among young women . Body Image , 2024; 49: 101715 DOI: 10.1016/j.bodyim.2024.101715

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EDITORIAL article

This article is part of the research topic.

Artificial Intelligence in Cutaneous Lesions: Where do we Stand and What is Next?

Editorial: "Artificial Intelligence in Cutaneous Lesions: Where do We Stand and What is next?" Provisionally Accepted

• 1 Albert Einstein Israelite Hospital, Brazil
• 2 Stanford University, United States

The final, formatted version of the article will be published soon.

The body of work spans a broad range, from skin cancer detection(2-4), inflammatory skin diseases (5,6) surveys with dermatologists (7), patients perspectives (8), among others (9,10) What is next in this field? To try answer these questions, this Special Research Topic solicited articles and resulted in 10 manuscripts from teams diverse in geographic representation as well as topic were accepted and published to shed light on these questions.In setting the stage to answer the question "where are we now?", Furriel et al provided a systematic review of papers specifically on AI as applied to the detection, classification, and assessment of skin cancer images in the clinical setting. Their rigorous methodology identified 18 studies that encompassed a diversity of approaches in skin cancer detection, as well as significant differences in dataset size. They highlight the areas of convergence and divergence in the work and approaches to this topic, including more focused binary tools versus broader approaches with multiclass output.Two papers provide additional reflections on the state of the art as well as starting to answer the question "where are we going?". Omiye et al provided a broad overview of artificial intelligence (AI), as applied to dermatology with a primary focus on methodology, AI applications for various skin diseases, limitations, and future opportunities. They reviewed the current image-based models, highlighted the challenges facing widespread adoption and the future of AI in evolving the paradigm of large language, and multi-modal models. (https://www.frontiersin.org/articles/10.3389/fmed.2023.1288521/full) Second, they aimed to implement the above AI solution, and safely reduce referral rates. Their objective was to demonstrate that the AIaMD had a higher rate of correctly classifying lesions that did not need to be referred for biopsy or urgent face-to-face dermatologist review, compared to teledermatology standard of care (SoC), maintaining the same sensitivity to detect malignancy.Their results showed a potential to reduce the burden of unnecessary referrals when used as part of a teledermatology service.

Keywords: artificial intelligence - AI, Cutaneous diseases, Dermatology, Skin Cancer, Teledermatology, Acne, Patient survey, Sistematic literature review

Received: 19 Apr 2024; Accepted: 30 Apr 2024.

Copyright: © 2024 GIAVINA-BIANCHI and Ko. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: MD, PhD. MARA GIAVINA-BIANCHI, Albert Einstein Israelite Hospital, São Paulo, Brazil

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Title: enhanced visual question answering: a comparative analysis and textual feature extraction via convolutions.

Abstract: Visual Question Answering (VQA) has emerged as a highly engaging field in recent years, attracting increasing research efforts aiming to enhance VQA accuracy through the deployment of advanced models such as Transformers. Despite this growing interest, there has been limited exploration into the comparative analysis and impact of textual modalities within VQA, particularly in terms of model complexity and its effect on performance. In this work, we conduct a comprehensive comparison between complex textual models that leverage long dependency mechanisms and simpler models focusing on local textual features within a well-established VQA framework. Our findings reveal that employing complex textual encoders is not invariably the optimal approach for the VQA-v2 dataset. Motivated by this insight, we introduce an improved model, ConvGRU, which incorporates convolutional layers to enhance the representation of question text. Tested on the VQA-v2 dataset, ConvGRU achieves better performance without substantially increasing parameter complexity.

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