literature review monash university

BEW4020 - Literature review

• Subject guides
• Systematic Review
• Manuals, documentation & PRISMA

Systematic Review: Manuals, documentation & PRISMA

• Getting started
• Develop question & key concepts
• Look for existing reviews
• Scoping searches & gold set
• Identify search terms
• Select databases & grey literature sources
• Develop criteria & protocol
• Run your search
• Limits & filters
• Review & test your search
• Save & manage your search results
• Database search translation
• Screening process steps
• Assess quality of your included studies
• Request a consultation

Refer to relevant standards, guidelines or manuals for systematic reviews

If you conduct a systematic review for an organisation that commissions or sponsors reviews, such as Cochrane, you will need to adhere to their guidelines and standards. Some journals may also have particular requirements. For any review you are doing, we recommend that you follow a prescribed process.

Organisations that have developed manuals for systematic reviews include:

• Institute of Medicine ( US) standards
• Cochrane  handbook
• EPPI Centre
• JBI - Joanna Briggs Institute  manual

If you are doing a systematic review for publication, it is also worthwhile looking at SRs in the journals that you are considering submitting to. Different publications may have different standards as to the number of reviewers, the choice of databases searched, the documentation of methods, the registration of a protocol, or other factors.

Guidelines for other review types

There is a plethora of articles, books, and websites detailing methodologies and processes for creating other types of reviews that are considered 'systematic'. The resources below are a helpful beginning, as is the seminal article by Grant and Booth (2009)  (UK librarians) and this website by Canadian librarians:

Rapid Reviews:

See the NCCMT Rapid Review Guidebook

Cochrane advice: Updated recommendations for the Cochrane rapid review methods guidance for rapid reviews of effectiveness

Scoping Reviews:

See the JBI S coping Review Chapter in the JBI Manual

Umbrella Reviews:

See the JBI U mbrella Reviews Chapter in the JBI Manual

Importance of documentation

Systematic reviews should be transparent and replicable, requiring you to document each step as you progress. There is no one way to do this. Options include using Excel or Word, keeping a diary or journal, and creating personal database accounts for saving searches. Data collected for an SR should be accurate, complete, and in a format that allows for future updates and data sharing. EndNote libraries are an example of data formats to keep.

As with any data collection and reporting process, it's worthwhile keeping in mind basic data management principles:

• Back up work regularly and in multiple, different physical locations.

Use filenames and systems that timecode your entries and updates, to avoid different collaborators working on different versions.  (Using an electronic lab notebook such as LabArchives can be a useful way around this).

Data planning  and  Data storage  are crucial elements in your planning process.

Consider making your data open (fully or partially): Open access data may even be a requirement for some systematic review organisations or publications.  Find out more about open access data from the ARDC (Australian Research Data Commons) and from our  guide .

What is PRISMA?

Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) is an evidence-based minimum set of items for reporting in systematic reviews and meta-analyses. The PRISMA 2020 statement  aims to address ongoing issues of poorly documented review methods of published reviews, that lack transparency and reproducibility.

PRISMA resources include:

• PRISMA 2020 checklist - a checklist developed to improve the transparency of reporting. The checklist addresses the introduction, methods, results, and discussion sections of a systematic review report.
• PRISMA 2020 Flow Diagram - the flow diagram depicts the flow of information through the different phases of a systematic review. It maps out the number of records identified, included, and excluded, and the reasons for exclusions. Different templates are available depending on the type of review (new or updated) and sources used to identify studies.
• PRISMA 2020 Flow Diagram generator - a tool that can be utilised to automate the flow diagram process.
• PRISMA extensions - several extensions of the PRISMA Statement have been developed to facilitate the reporting of different types or aspects of systematic reviews. Examples include PRISMA-S for searching, PRISMA-SCR for scoping reviews, and PRISMA-P for protocols.

PRISMA-S for reporting the search

The PRISMA-S extension for searching was published in 2021 and is "a 16-item checklist that covers multiple aspects of the search process for systematic reviews. It is intended to guide reporting, not conduct, of the search." (Rethlefsen et al., 2021, p. 3)

Essential inclusions on the checklist are:

• Name of databases searched eg. MEDLINE
• Name of the database platform/interface eg. MEDLINE (OVID), CINAHL Plus (EBSCOhost)
• Full search strategies - for each database copied and pasted, justifications for limits and restrictions etc.

You may also find this article useful detailing some common questions regarding the PRISMA 2020 statement and PRISMA-S .

Other reporting guidelines

EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network is an international initiative that seeks to improve the value of published health literature by promoting transparent and accurate reporting via the use of robust reporting guidelines. The Equator Network site includes 549 reporting guidelines covering all areas of research study design. This includes PRISMA for systematic reviews and meta-analyses, and the AGREE reporting checklist for clinical practice guidelines.

MECCIR (Methodological Expectations of Campbell Collaboration Intervention Reviews) was developed to guide the conduct and reporting of systematic reviews. It includes checklists and other associated tools.

MECIR  (Methodological Expectations of Cochrane Intervention Reviews) are "standards for the conduct and reporting of new Cochrane Intervention Reviews, reporting of protocols and the planning, conduct and reporting of updates".

PRIOR  (Preferred Reporting Items for Overviews of Reviews) are "A reporting guideline based on evidence and agreement aims to facilitate improvements in the complete and accurate reporting of overviews of reviews".

ROSES  (RepOrting standards for Systematic Evidence Syntheses) in environmental research includes forms and flow diagram.

Additional PRISMA 2020 resources

• Excel PRISMA 2020 flow diagram calculator This tool allows you to check your manual calculations to ensure your PRISMA flow diagram is accurate.
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This unit entry is for students who completed this unit in 2016 only. For students planning to study the unit, please refer to the unit indexes in the the current edition of the Handbook . If you have any queries contact the managing faculty for your course or area of study.

PSY4402 - Psychology research project: Literature review

6 points, sca band 1, 0.125 eftsl.

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.

Medicine, Nursing and Health Sciences

Organisational Unit

School of Psychological Sciences

Coordinator(s)

Dr Stephen McKenzie

Monash Online

• Teaching Period 5 2016 (Online)

The overall GDPA research project is divided into three units, PSY4402, PSY4403 AND PSY4404, beginning with this unit, PSY4402. The collective aim of these units is to increase students' understanding of theoretical and methodological aspects of research, develop analytic, research and communication skills, as well as provide students with advanced knowledge in the science and practice of psychology. The research project meets the requirements of the Australian Psychological Society for Associate Membership. The aim of PSY4402 is to enable students to produce a high quality research project component, consisting of the production of a research report introduction/ literature review. Students will undertake a supervised research project that aims to provide training in both discipline specific and generic research skills, and form the basis of a literature review and research report presented at the end of the course. The thesis introduction/ literature review component of the research report will consist of a description of the students' project, a detailed review of the relevant literature, and a statement of the research hypotheses to be tested. Students will be provided with course materials and guidance to help them produce a high quality research proposal, source and synthesise appropriate research findings, and coherently synthesise this into a research project. The development of these skills and their application to the production of the introduction/ literature review component of the students' research project will build on their research and writing skills developed in previous courses.

Upon successful completion of this unit, students should be able to:

• Explain how their own research fits into a broader research context;
• Produce a testable proposal for research that will contribute to the development of knowledge in a particular area of psychology;
• Develop and demonstrate high level skill in sourcing, reading and synthesising relevant psychological literature into a coherent research review;
• Generate coherent and testable hypotheses that will allow a contribution to the development of knowledge in a particular area of psychology;
• Explain how their data collection, analyses and interpretation will be informed by the need to advance existing knowledge in a particular area of psychology.

Oral presentation - research proposal (10 minutes presentation, 5 minutes of questions)(25%) Literature Review - project introduction (2000 - 3000 words)(50%)(Hurdle) Project introduction including hypothesis generation (750 - 1000 words)(25%)

Workload requirements

Students should expect to spend an average of 20-25 hours per week per unit. Broadly, this time will cover: Video materials (2hrs) Synchronous & asynchronous discussion including with other students and with their research project supervisors (2hrs) Synchronous web-based 'class-time' for skills-based activities (2hrs).

See also Unit timetable information

Chief examiner(s)

This unit applies to the following area(s) of study, prerequisites.

Completed Bachelor's Degree and a major sequence in psychology approved by the Australian Psychological Society or qualification assessed as equivalent by the Australian Psychological Society, with a distinction average (70%) for third year psychology units.

Co-requisites

Must be enrolled in Graduate Diploma of Psychology Advanced (M5003)

Prohibitions

PSY4001, PSY4002, PSY4011, PSY4012, PSY4021, PSY4022, PSY4100 and PSY4210

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Barriers and Enablers to Return to Work: Literature Review

Report prepared for Work Safe Victoria providing a theoretical and empirical background for the factors influencing return to work after injury. 

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Pachymeningeal enhancement—a comprehensive review of literature

Research output : Contribution to journal › Review Article › Research › peer-review

Pachymeningeal enhancement, synonymous with dural enhancement, is a radiological feature best appreciated on a contrast-enhanced magnetic resonance imaging (MRI). The vasculature of the dura mater is permeable, facilitating avid uptake of contrast agent and subsequent enhancement. Thin, discontinuous enhancement can be normal, seen in half the normal population. In patients complaining of postural headaches worse on sitting, gadolinium-enhanced MRI findings of diffuse pachymeningeal enhancement is highly suggestive of benign intracranial hypotension. In these cases, the process of pachymeningeal enhancement is explained by the Monro–Kellie doctrine as compensatory volume changes by vasocongestion and interstitial oedema of the dura mater due to decreased cerebrospinal fluid (CSF) pressure. Focal and diffuse pachymeningeal enhancement can also be attributed to infectious or inflammatory, neoplastic and iatrogenic aetiologies. Correction of the underlying pathology often results in spontaneous resolution of the pachymeningeal enhancement. There have also been reports of pachymeningeal enhancement associated with cerebral venous sinus thrombosis, temporal arteritis, baroreceptor reflex failure syndrome and arteriovenous fistulae.

• Enhancement
• Pachymeningeal

Access to Document

• 10.1007/s10143-015-0646-y

Other files and links

• Link to publication in Scopus

T1 - Pachymeningeal enhancement—a comprehensive review of literature

AU - Antony, Joyce

AU - Hacking, Craig

AU - Jeffree, Rosalind L.

N1 - Publisher Copyright: © 2015, Springer-Verlag Berlin Heidelberg.

PY - 2015/10

Y1 - 2015/10

N2 - Pachymeningeal enhancement, synonymous with dural enhancement, is a radiological feature best appreciated on a contrast-enhanced magnetic resonance imaging (MRI). The vasculature of the dura mater is permeable, facilitating avid uptake of contrast agent and subsequent enhancement. Thin, discontinuous enhancement can be normal, seen in half the normal population. In patients complaining of postural headaches worse on sitting, gadolinium-enhanced MRI findings of diffuse pachymeningeal enhancement is highly suggestive of benign intracranial hypotension. In these cases, the process of pachymeningeal enhancement is explained by the Monro–Kellie doctrine as compensatory volume changes by vasocongestion and interstitial oedema of the dura mater due to decreased cerebrospinal fluid (CSF) pressure. Focal and diffuse pachymeningeal enhancement can also be attributed to infectious or inflammatory, neoplastic and iatrogenic aetiologies. Correction of the underlying pathology often results in spontaneous resolution of the pachymeningeal enhancement. There have also been reports of pachymeningeal enhancement associated with cerebral venous sinus thrombosis, temporal arteritis, baroreceptor reflex failure syndrome and arteriovenous fistulae.

AB - Pachymeningeal enhancement, synonymous with dural enhancement, is a radiological feature best appreciated on a contrast-enhanced magnetic resonance imaging (MRI). The vasculature of the dura mater is permeable, facilitating avid uptake of contrast agent and subsequent enhancement. Thin, discontinuous enhancement can be normal, seen in half the normal population. In patients complaining of postural headaches worse on sitting, gadolinium-enhanced MRI findings of diffuse pachymeningeal enhancement is highly suggestive of benign intracranial hypotension. In these cases, the process of pachymeningeal enhancement is explained by the Monro–Kellie doctrine as compensatory volume changes by vasocongestion and interstitial oedema of the dura mater due to decreased cerebrospinal fluid (CSF) pressure. Focal and diffuse pachymeningeal enhancement can also be attributed to infectious or inflammatory, neoplastic and iatrogenic aetiologies. Correction of the underlying pathology often results in spontaneous resolution of the pachymeningeal enhancement. There have also been reports of pachymeningeal enhancement associated with cerebral venous sinus thrombosis, temporal arteritis, baroreceptor reflex failure syndrome and arteriovenous fistulae.

KW - Enhancement

KW - Pachymeningeal

UR - http://www.scopus.com/inward/record.url?scp=84941314630&partnerID=8YFLogxK

U2 - 10.1007/s10143-015-0646-y

DO - 10.1007/s10143-015-0646-y

M3 - Review Article

C2 - 26264063

AN - SCOPUS:84941314630

SN - 0344-5607

JO - Neurosurgical Review

JF - Neurosurgical Review

AI-assisted writing is quietly booming in academic journals. Here’s why that’s OK

Lecturer in Bioethics, Monash University & Honorary fellow, Melbourne Law School, Monash University

Disclosure statement

Julian Koplin does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.

Monash University provides funding as a founding partner of The Conversation AU.

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If 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 “plagiarising” text authored by humans and text authored by AI. Those who plagiarise 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 realising 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.

• Artificial intelligence (AI)
• Academic journals
• Academic publishing
• Hallucinations
• Scholarly publishing
• Academic writing
• Large language models
• Generative AI

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