Raising awareness of anti-fat stigma in healthcare through lived experience education: a continuing professional development pilot study
Bibliographic record
Abstract
BACKGROUND: Anti-fat attitudes and weight-based discrimination are prevalent in healthcare settings and among healthcare practitioners and clinical trainees, and can result in immense harm to patients. There is increasing recognition that anti-fat bias in healthcare is a critical issue that must be addressed, but there is a dearth of evidence demonstrating sustained attitude and behavioural change among clinicians, illustrating a need for more innovative educational approaches and rigorous evaluation. We describe the co-design and delivery of a narrative-based continuing professional development curriculum aimed at raising awareness of weight-based bias and stigma. METHODS: Our research team of lived experience educators, clinicians and researchers collaboratively developed a series of seven podcast episodes comprised of narrative descriptions of lived experiences with and impacts of weight bias, stigma and discrimination in healthcare settings, as well as a post-podcast workshop to facilitate reflection and discussion between participants. The curriculum was piloted among 20 clinicians practicing at a large urban hospital in Mississauga, Canada. We explored feasibility, acceptability and learning impact by analyzing responses to questionnaires completed following each podcast episode and responses shared during the workshops and follow-up feedback sessions. RESULTS: We observed high acceptability and feasibility of the curriculum. Participants experienced the podcast as a practical and convenient learning format and the workshop as a valuable opportunity to collectively debrief and reflect. The learning impact of the curriculum was strong; participants described a range of emotions elicited by the podcasts, engaged in self-reflection, and expressed a desire to modify clinical approaches. Barriers to the application of learnings identified by participants include pervasiveness of the use of body mass index (BMI) as an indicator of risk and a criterion for referral; discomfort with difficult conversations; prevalent biomedical understandings about the association between weight and health; and clinicians' defensiveness. CONCLUSION: This pilot study yielded promising findings and demonstrated potential impact on weight bias and stigma among healthcare providers. Necessary next steps include conducting larger scale, rigorous evaluations of the curriculum among broader populations, both health professions trainees and current healthcare providers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".