https://orcid.org/0000-0002-9322-717X
Figures
Abstract
Weight bias is pervasive in health care, contributing to adverse physical and psychological outcomes for affected individuals. While weight bias has been studied in various healthcare contexts, its presence and impact within medical radiation science remain underexplored. This systematic review aims to synthesise the existing literature on the assessment of weight bias in medical radiation science. This protocol is registered with PROSPERO (CRD42024506662). Comprehensive searches will be conducted across multiple electronic databases using predefined search strategies. Retrieved records will be imported into Covidence for title and abstract screening, followed by full-text review. Two independent reviewers will assess study eligibility and extract data. Methodological quality will be evaluated using a multi-tool appraisal approach tailored to the design of each study. Risk-of-bias will be assessed, and meta-biases will be explored through the inclusion of grey literature. Confidence in the cumulative evidence will be evaluated using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. This systematic review protocol will provide a comprehensive synthesis of the quantitative assessment of weight bias among professionals and students in medical radiation science. By mapping how weight bias is measured, the findings may support awareness-raising efforts and inform development of strategies to reduce weight bias in education and clinical practice. Once complete, the review will be shared internationally via presentations or posters at conferences. It will be published in a peer-reviewed journal.
Citation: O’ Donovan T, Brydon M, Barry A, McEntee M (2026) Assessment of weight bias among students and health professionals in medical radiation science. A protocol for a systematic review and meta-analysis. PLoS One 21(3): e0345171. https://doi.org/10.1371/journal.pone.0345171
Editor: Mickael Essouma, Independent Medical Researcher and Writer, UNITED KINGDOM OF GREAT BRITAIN AND NORTHERN IRELAND
Received: October 3, 2025; Accepted: March 3, 2026; Published: March 25, 2026
Copyright: © 2026 O’ Donovan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: This study is a protocol for a systematic review and does not involve the collection of primary data. All materials relevant to the conduct of the review (e.g., search strategy, data extraction form, PRISMA-P checklist) are available via PROSPERO (CRD42024506662) and will be made publicly available via the Cork Open Research Archive (CORA) at https://cora.ucc.ie. Materials will be searchable through Google and Google Scholar and will be harvested by aggregators like Open AIRE. These materials will be openly accessible under a CC-BY 4.0 license, in accordance with PLOS ONE’s data sharing policy.
Funding: The author(s) received no specific funding for this work.
Competing interests: The authors have declared that no competing interests exist.
Background and rationale
Globally, the average weight of the population is increasing. In 2022, the World Health Organisation (WHO) reported that 2.5 billion adults were “overweight”, and this figure is set to rise [1]. These projections will change the demographic profile of patients accessing healthcare services.
Weight bias refers to negative stereotypes, prejudicial attitudes, perceptions and judgements directed at individuals based on their body weight [2–4]. This type of bias can be implicit, which is described as unconscious bias or explicit where individuals consciously endorse such biases [5–8]. Implicit bias is most commonly measured in healthcare research using The Implicit Association Test [9]. This is a reaction-time-based measure where individuals are required to pair certain attributes with one group of people compared to another. Explicit bias is commonly assessed using a variety of self-report measures. A recent systematic review revealed 26 different measures in use to assess explicit bias in healthcare professionals [10].
Weight bias is common in all areas of society including the workplace, education, media and interpersonal relationships [11–15]. Individuals experience weight bias from a young age with teachers assigning lower grades to those with higher weight [12]. It can also manifest in the workplace, where individuals have been instructed to lose weight to keep their job or where job applications were rejected on the basis of weight [16,17].
Weight bias in healthcare is also pervasive. Bias towards individuals of higher weight is well established in multiple health care professions, even those who are involved in weight loss and weight management [18–34]. Research demonstrates that healthcare professionals often perceive people with higher weight as awkward, unattractive, and non-compliant [35]. The presence of weight bias and stigma within healthcare can impact patients negatively, causing a loss of trust in healthcare providers, as well as healthcare avoidance behaviours, such as reduced engagement with screening programs and delays in seeking treatment [36–41]. It may also contribute to lowered self-esteem, heightened anxiety, depression and chronic stress [42,43].
Explicit bias can manifest as misdiagnosis, altered clinical management, inadequate care, a lack of appropriate equipment (gowns, chairs, etc.), and inappropriate comments about weight [44–46]. Of greater concern is the insidious manifestation, such as health care professionals avoiding eye contact, not listening, engaging in shorter consultations or speaking in derogatory terms with other colleagues [39,47–53].
Evidence of weight bias has been documented within medical radiation science literature where articles with titles such as “what do we know about a big issue” have been published [54]. Individuals with higher body weight have been characterised as presenting challenges, causing inconvenience, or eliciting discomfort among practitioners [54–61].
Individuals who encounter professionals within medical radiation science are often in a vulnerable position, potentially undergoing investigations based on symptoms, or they are receiving treatment for a cancer diagnosis. There is a paucity of synthesised evidence focusing specifically on the assessment of weight bias in medical radiation science. The impact of weight bias within this sphere can lead to patient discomfort, reduced trust, and avoidance of care [49].
A review is warranted to systematically synthesise the research on the quantitative measurement of weight bias among professionals and trainees in medical radiation science. This systematic review aims to synthesise and critically appraise the evidence on quantitative tools (Intervention) used to assess weight bias (Outcome) among students and health professionals in medical radiation science (Population) within education, training and clinical practice contexts.
The aim of the protocol is to outline the methods that will be utilised in this systematic review.
Methods
We will follow the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines [62].
Information sources
A systematic search will be performed in five electronic databases: CINAHL Plus with Full Text (EBSCO), Embase, PubMed, PsycINFO (EBSCOHost) and Web of Science. Authors of conference abstracts will be contacted for access to full-text articles if available. Reference lists of articles retrieved during preliminary searches, individual authors, and key journals will be hand searched through Google Scholar.
Search strategy
A systematic search of the literature on assessment of students and health professionals within medical radiation science will be conducted using five electronic databases (CINAHL, Embase, PubMed, PsycINFO [EBSCOHost] and Web of Science). Controlled vocabulary and keywords will be used to search for terms for medical radiation science health professionals and their students/trainees, obesity, bias and assessment. Keywords for each concept will be identified through a preliminary review of the literature. Guidance and advice will be sought from institutional librarian services to further refine the strategies. Searches will be combined using Boolean operators. No restrictions on the year of publication will be imposed. No language restrictions will be imposed. To overcome publication bias, citation chasing and grey literature will be searched. Searches will be updated before submission of the systematic review for publication. A search strategy has been included in a supplementary file.
Study selection process
Studies will be selected based on eligibility criteria (Table 1). All eligible studies will be imported into Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia) [63]. Two reviewers will independently assess each title and abstract against the inclusion and exclusion criteria. Once complete, we will conduct a full text review of the remaining articles. If eligibility remains unclear, we will contact study corresponding authors via email (max of two email attempts) to obtain additional information. Any disagreements will be discussed and resolved by consensus. A third party will be consulted if the first two reviewers cannot reach an agreement. We will document specific reasons for exclusion of studies.
Data extraction
Data extraction will be conducted by two reviewers independently. A data extraction tool will be developed for this review in Microsoft Excel (version 2506, Microsoft Corporation, Redmond, WA, USA). Included elements are informed by preliminary research in this area and the knowledge of research team. Covidence systematic review software (Veritas Health Innovation, Melbourne, Australia) will be used to manage and extract these data [63]. Data items to be collected are displayed in Table 2. Disagreements will be resolved through discussion and consultation with a third party where required (another researcher within the team).
Quality assessment
To assess the methodological quality of included studies, we will employ a multi-tool appraisal approach tailored to the specific design of each study. This strategy will ensure that each study type will be evaluated using criteria most appropriate to its methodological framework.
- Cross-sectional studies will be assessed using the CASP Cross-Sectional Checklist, focusing on sampling strategy, measurement validity, and risk of bias [66].
- Mixed-methods studies, where applicable, will be reviewed using the Mixed Methods Appraisal Tool (MMAT) [67] provided the study design aligns with MMAT’s criteria.
Two reviewers will independently appraise each study. Discrepancies will be resolved through discussion or consultation with a third reviewer. Appraisal results will be used to inform the synthesis and interpretation of findings, but no studies will be excluded solely based on quality scores.
Data synthesis
A narrative synthesis will be undertaken, supported by a summary of findings and a data extraction table. Study characteristics will be analysed to facilitate comparisons.
For the studies where a meta-analysis is possible, pooled estimates will be calculated using a random-effects model. Effect sizes will be reported as standardised mean differences (Hedges g). Heterogeneity will be assessed with Cochrane’s Q and I2. If the Cochrane’s Q statistic suggests the presence of heterogeneity (p < 0.10), the degree of inconsistency across studies will be quantified using the I² statistic. In accordance with conventional interpretation guidelines, I² values will be categorised as low (0–40%), moderate (41–60%), and substantial heterogeneity [62] (61–100%).
Subgroup and sensitivity analyses
Where sufficient data are available, we will conduct subgroup analyses to explore potential sources of heterogeneity in study findings. Subgroup analyses will be based on participant characteristics (e.g., gender, ethnicity, age, body mass index, profession, year of study or years of professional experience). Any meta-analysis will be completed using Comprehensive Meta-Analysis (CMA) version 4.0. Sensitivity analysis will test robustness by comparing fixed‑ vs. random‑effects models and restricting to validated measurement tools or rigorous qualitative methodologies.
Assessment of Meta-bias(es)
To assess and reduce publication bias, a search of the grey literature will be conducted. If a meta-analysis is possible, then a funnel plot can be used to visually detect asymmetry, which is suggestive of publication bias. Any available protocols will be compared to final publications to overcome selective outcome reporting.
Confidence in cumulative evidence
We will apply the Grading of recommendations Assessment, Development and Evaluation (GRADE) system to evaluate the certainty of evidence across outcomes [68]. A Summary of Findings (SOF) table will be developed for key outcomes using GRADE evidence profiles. A timeline of the project is provided (Table 3)
Patient and public involvement
This review will not involve patients or members of the public in its design, conduct, reporting, or dissemination. The study is based exclusively on previously published literature and does not include primary data collection or stakeholder engagement.
Ethical considerations
As this study is a systematic review of published literature, it does not involve human participants, personal data, or interventions. Therefore, ethical approval was not required. All included studies will be assessed for ethical compliance as part of the appraisal process.
Protocol deviation
Any deviations from the protocol that occur during the conduct of the review will be documented and reported in the final manuscript. The date of changes and rationale will be provided, and their impact on the process will be discussed transparently.
Data sharing
Two reviewers will independently extract data (study characteristics, populations, outcomes, and risk-of-bias assessments) from eligible articles and grey literature. Covidence will be used to store and organise all data during the review. Data will be securely stored on institutional servers with regular backups. A pilot test of the extraction form will be conducted on a sample of studies to ensure clarity and consistency. Regular checks will be performed to verify accuracy and completeness of extracted data. Upon completion of the review, anonymised datasets and extraction forms will be made available via an institutional repository, subject to ethical and legal considerations. Metadata and documentation will be provided to facilitate reuse. Any changes will be documented and updated in PROSPERO with justification.
Discussion
This will be the first systematic review to synthesise quantitative assessment of weight bias in medical radiation science, addressing a critical gap in the existing literature. Many studies involving medical radiation science students and health professionals have only emerged in the past three years and therefore have not been represented in previous reviews of healthcare professionals more broadly.
By examining how weight bias has been measured in medical radiation science contexts – where patient encounters frequently involve physical exposure and heightened vulnerability- this review has the potential to highlight an underexplored and ethically significant issue. Variability in study designs, populations and measurement tools may limit direct comparisons, nonetheless, efforts will be made to ensure rigour throughout the screening and extraction process.
Conclusion
This systematic review will synthesise the quantitative assessment of weight bias among medical radiation science students and health professionals. By mapping how weight bias has been measured in this population and identifying existing gaps, the findings may support the development of future awareness initiatives and inform strategies aimed at reducing weight bias in both education and clinical settings. Ultimately this work has the potential to contribute to more equitable, patient-centred care in medical radiation science. On completion the findings will be disseminated through conference presentations, posters and publication in a peer-reviewed journal.
Acknowledgments
The authors would like to acknowledge the guidance and assistance of Virgina Wade and Siobhan Bowman, University College Cork Librarians during the search strategy design stage. Microsoft co-pilot was used to identify relevant synonyms during the development of search terms and to generate elements included in data extraction.
References
- 1.
World Health Organisation. Obesity and overweight. 2024 [cited 2025 Feb 14]. Available from: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight
- 2. Andreyeva T, Puhl RM, Brownell KD. Changes in perceived weight discrimination among Americans, 1995–1996 through 2004–2006. Obesity. 2008;16(5):1129–34.
- 3. Brewis A, Sturtz Sreetharan C, Wutich A. Obesity stigma as a globalizing health challenge. Glob Health. 2018;14(1):20.
- 4. Lee M, Ata RN, Brannick MT. Malleability of weight-biased attitudes and beliefs: a meta-analysis of weight bias reduction interventions. Body Image. 2014;11(3):251–9. pmid:24958660
- 5. Allison DB, Basile VC, Yucker HE. The measurement of attitudes toward and beliefs about obese persons. Int J Eat Disord. 1991;10(5):599–607.
- 6. Bacon JG, Scheltema KE, Robinson BE. Fat phobia scale revisited: the short form. Int J Obes Relat Metab Disord. 2001;25(2):252–7. pmid:11410828
- 7. Crandall CS. Prejudice against fat people: ideology and self-interest. J Pers Soc Psychol. 1994;66(5):882–94. pmid:8014833
- 8. Greenwald AG, Poehlman TA, Uhlmann EL, Banaji MR. Understanding and using the Implicit Association Test: III. Meta-analysis of predictive validity. J Pers Soc Psychol. 2009;97(1):17–41. pmid:19586237
- 9. Greenwald AG, McGhee DE, Schwartz JL. Measuring individual differences in implicit cognition: the implicit association test. J Pers Soc Psychol. 1998;74(6):1464–80. pmid:9654756
- 10. Lawrence BJ, Kerr D, Pollard CM, Theophilus M, Alexander E, Haywood D, et al. Weight bias among health care professionals: a systematic review and meta-analysis. Obesity (Silver Spring). 2021;29(11):1802–12. pmid:34490738
- 11. Lucibello KM, Vani MF, Koulanova A, de Jonge ML, Ashdown-Franks G, Sabiston CM. #quarantine15: A content analysis of Instagram posts during COVID-19. Body Image. 2021;38:148–56.
- 12. Finn KE, Seymour CM, Phillips AE. Weight bias and grading among middle and high school teachers. Br J Educ Psychol. 2020;90(3):635–47. pmid:31654405
- 13. Bellizzi JA, Hasty RW. Territory assignment decisions and supervising unethical selling behavior: the effects of obesity and gender as moderated by job-related factors. J Personal Selling Sales Manag. 1998;18(2):35–49.
- 14. Larkin JC, Pines HA. No fat persons need apply: experimental studies of the overweight stereotype and hiring preference. Sociol Work Occup. 1979;6(3):312–27.
- 15. Eriksson A, Horton P. ‘How can you be friends with that fatty?’: The othered body in narratives on weight-based bullying. Child Soc. 2025;39(1):146–60.
- 16.
Human Rights Tribunal of Ontario/Board of Inquiry. Maddox v. Vogue Shoes, Board of Inquiry. 1991. http://archive.org/details/boi91_004
- 17.
Davison v. St. Paul Lutheran Home of Melville, Sask. et al., (1992) 107 Sask.R. 31 (QB). https://ca.vlex.com/vid/davison-v-st-paul-680747261. Accessed 2025 April 4.
- 18. Remmert JE, Convertino AD, Roberts SR, Godfrey KM, Butryn ML. Stigmatizing weight experiences in health care: Associations with BMI and eating behaviours. Obes Sci Pract. 2019;5(6):555–63. pmid:31890246
- 19. Elboim-Gabyzon M, Attar K, Peleg S. Weight Stigmatization among Physical Therapy Students and Registered Physical Therapists. Obes Facts. 2020;13(2):104–16. pmid:32074613
- 20. Forhan M, Risdon C, Solomon P. Contributors to patient engagement in primary health care: perceptions of patients with obesity. Prim Health Care Res Dev. 2013;14(4):367–72. pmid:23237022
- 21. Hebl MR, Xu J. Weighing the care: physicians’ reactions to the size of a patient. Int J Obes Relat Metab Disord. 2001;25(8):1246–52. pmid:11477511
- 22. Jung FUCE, Luck-Sikorski C, Wiemers N, Riedel-Heller SG. Dietitians and Nutritionists: Stigma in the Context of Obesity. A Systematic Review. PLoS One. 2015;10(10):e0140276. pmid:26466329
- 23. Miller DPJ, Spangler JG, Vitolins MZ, Davis SW, Ip EH, Marion GS. Are medical students aware of their anti-obesity bias?. Acad Med. 2013;88(7):978.
- 24. Murphy AL, Gardner DM. A scoping review of weight bias by community pharmacists towards people with obesity and mental illness. Can Pharm J (Ott). 2016;149(4):226–35. pmid:27540405
- 25. Oberrieder H, Walker R, Monroe D, Adeyanju M. Attitude of dietetics students and registered dietitians toward obesity. J Am Diet Assoc. 1995;95(8):914–6. pmid:7636085
- 26. Poon M-Y, Tarrant M. Obesity: attitudes of undergraduate student nurses and registered nurses. J Clin Nurs. 2009;18(16):2355–65. pmid:19374692
- 27. Price JH, Desmond SM, Krol RA, Snyder FF, O’Connell JK. Family practice physicians’ beliefs, attitudes, and practices regarding obesity. Am J Prev Med. 1987;3(6):339–45.
- 28. Sabin JA, Marini M, Nosek BA. Implicit and explicit anti-fat bias among a large sample of medical doctors by BMI, race/ethnicity and gender. PLoS One. 2012;7(11):e48448. pmid:23144885
- 29. Ward-Smith P, Peterson JA. Development of an instrument to assess nurse practitioner attitudes and beliefs about obesity. J Am Assoc Nurse Pract. 2016;28(3):125–9. pmid:26178582
- 30. Wise FM, Harris DW, Olver JH. Attitudes to obesity among rehabilitation health professionals in Australia. J Allied Health. 2014;43(3):162–8. pmid:25194063
- 31. Wolf C. Physician assistants’ attitudes about obesity and obese individuals. J Allied Health. 2012;41(2):e45-8. pmid:22735825
- 32. Young LM, Powell B. The effects of obesity on the clinical judgments of mental health professionals. J Health Soc Behav. 1985;26(3):233–46. pmid:4067239
- 33. Chambliss HO, Finley CE, Blair SN. Attitudes toward obese individuals among exercise science students. Med Sci Sports Exerc. 2004;36(3):468–74. pmid:15076789
- 34. Klobodu SS, Mensah PA, Willis M, Bailey D. Weight Bias Among Nutrition and Dietetics Students in a Ghanaian Public University. J Nutr Educ Behav. 2022;54(5):406–11. pmid:35351356
- 35. Foster GD, Wadden TA, Makris AP, Davidson D, Sanderson RS, Allison DB, et al. Primary care physicians’ attitudes about obesity and its treatment. Obes Res. 2003;11(10):1168–77. pmid:14569041
- 36. Friedman AM, Hemler JR, Rossetti E, Clemow LP, Ferrante JM. Obese women’s barriers to mammography and pap smear: the possible role of personality. Obesity (Silver Spring). 2012;20(8):1611–7. pmid:22370590
- 37. McBride KA, Munasinghe S, Sperandei S, Page A. The longitudinal role of overweight and obesity women in mammographic breast screening participation: retrospective cohort study using linked data. medRxiv. 2024;:2024.01.08.24301020.
- 38. Hellmann SS, Njor SH, Lynge E, von Euler-Chelpin M, Olsen A, Tjønneland A, et al. Body mass index and participation in organized mammographic screening: a prospective cohort study. BMC Cancer. 2015;15:294. pmid:25880028
- 39. McBride KA, Fleming CAK, George ES, Steiner GZ, MacMillan F. Double discourse: qualitative perspectives on breast screening participation among obese women and their health care providers. Int J Environ Res Public Health. 2019;16(4):534. pmid:30781792
- 40. Incollingo Rodriguez AC, Dunkel Schetter C, Tomiyama AJ. Weight stigma among pregnant and postpartum women: A new context of stigmatization. Stigma and Health. 2020;5(2):209–16.
- 41. Byrd R, Dolbier C, Whited M, Carels RA. The role of weight stigma in health care avoidance and mistrust among pregnant women. Stigma and Health. 2023.
- 42. Puhl RM, Latner JD, O’Brien K, Luedicke J, Danielsdottir S, Forhan M. A multinational examination of weight bias: predictors of anti-fat attitudes across four countries. Int J Obes (Lond). 2015;39(7):1166–73. pmid:25809827
- 43. Puhl R, Suh Y. Health consequences of weight stigma: implications for obesity prevention and treatment. Curr Obes Rep. 2015;4(2):182–90.
- 44. Puhl RM, Brownell KD. Confronting and coping with weight stigma: an investigation of overweight and obese adults. Obesity (Silver Spring). 2006;14(10):1802–15. pmid:17062811
- 45. Amy NK, Aalborg A, Lyons P, Keranen L. Barriers to routine gynecological cancer screening for White and African-American obese women. Int J Obes (Lond). 2006;30(1):147–55. pmid:16231037
- 46. Murray TE, Ma SD, Doyle F, Lee MJ. Radiology reporting of obesity: a survey of patient and clinician attitudes. Clin Radiol. 2018;73(5):506.e9-506.e15. pmid:29534789
- 47. Puhl RM, Heuer CA. The stigma of obesity: a review and update. Obesity (Silver Spring). 2009;17(5):941–64. pmid:19165161
- 48. Anderson DA, Wadden TA. Bariatric surgery patients’ views of their physicians’ weight-related attitudes and practices. Obes Res. 2004;12(10):1587–95. pmid:15536222
- 49. Brydon M. Weight bias: A consideration for medical radiation sciences. J Med Imaging Radiat Sci. 2022;53(4):534–7. pmid:36155175
- 50. Bolderston A, Brydon M, MacLaine J, Carr M. A Review of the Issues Associated with Imaging and Treating Patients with a Larger Body Habitus. Journal of Medical Imaging and Radiation Sciences. 2022;53(2):S15–6.
- 51. Smith S, Smoke M, Farrell T, Reis V, Brydon M. Does size matter? Weight bias, stigma, and medical radiation technology practice in Canada. J Med Imaging Radiat Sci. 2025;56(3):101886. pmid:40090064
- 52. Richard P, Ferguson C, Lara AS, Leonard J, Younis M. Disparities in physician-patient communication by obesity status. Inquiry. 2014;51:0046958014557012. pmid:25432989
- 53. Bertakis KD, Azari R. The impact of obesity on primary care visits. Obes Res. 2005;13(9):1615–23. pmid:16222065
- 54. Le NTT, Robinson J, Lewis SJ. Obese patients and radiography literature: what do we know about a big issue?. J Med Radiat Sci. 2015;62(2):132–41. pmid:26229678
- 55. Winters E, Poole C. Challenges and impact of patient obesity in radiation therapy practice. Radiography (Lond). 2020;26(3):e158–63. pmid:32052747
- 56. Miller PK, Woods AL, Sloane C, Booth L. Obesity, heuristic reasoning and the organisation of communicative embarrassment in diagnostic radiography. Radiography (Lond). 2017;23(2):130–4. pmid:28390544
- 57. Thanh LeNT, Robinson J, Lewis SJ. A Study of Student Radiographers’ Learning Experiences in Imaging Obese Patients. J Med Imaging Radiat Sci. 2015;46(3):S61–8. Located at: Embase.
- 58. Destounis S, Newell M, Pinsky R. Breast imaging and intervention in the overweight and obese patient. AJR Am J Roentgenol. 2011;196(2):296–302. pmid:21257879
- 59. Uppot RN. Technical challenges of imaging & image-guided interventions in obese patients. Br J Radiol. 2018;91(1089):20170931. pmid:29869898
- 60. Carucci LR. Imaging obese patients: problems and solutions. Abdom Imaging. 2013;38(4):630–46. pmid:23008055
- 61. van den Heuvel J, Punch A, Aweidah L, Meertens R, Lewis S. Optimizing projectional radiographic imaging of the abdomen of obese patients: An e-Delphi study. J Med Imaging Radiat Sci. 2019;50(2):289–96.
- 62. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. pmid:33782057
- 63.
Covidence - Better systematic review management. https://www.covidence.org/. Accessed 2025 May 29.
- 64.
Take a test. https://implicit.harvard.edu/implicit/takeatest.html. Accessed 2025 June 13.
- 65. Greenwald AG, McGhee DE, Schwartz JLK. Measuring individual differences in implicit cognition: The implicit association test. J Pers Soc Psychol. 1998;74(6):1464–80.
- 66.
C A S P - Critical Appraisal Skills Programme. Cross sectional studies checklist - CASP. https://casp-uk.net/casp-tools-checklists/cross-sectional-studies-checklist/. Accessed 2025 December 15.
- 67. Hong QN, Fàbregues S, Bartlett G, Boardman F, Cargo M, Dagenais P, et al. The Mixed Methods Appraisal Tool (MMAT) version 2018 for information professionals and researchers. Educ Inf. 2018;34(4):285–91.
- 68.
Ryan R, Hill S. How to GRADE. http://doi.org/10.26181/5b57d95632a2c. 2018.