Embedding interpersonal stigma resistance into the medical curriculum: a focus group study of medical students
Bibliographic record
Abstract
BACKGROUND: Mental-health-related stigma among physicians towards people with mental illnesses remains a barrier to quality care, yet few curricula provide training with a proactive focus to reduce the potential negative impacts of stigma. The aim of our study was to explore medical students' perspectives on what areas of learning should be targeted (where stigma presents) and how they could be supported to prevent the formation of negative attitudes. METHODS: Six focus group discussions were conducted with second, third, and fourth-year postgraduate medical students (n = 34) enrolled at The University of Melbourne Medical School in September - October 2021. Transcripts were analysed using inductive thematic analysis. RESULTS: In terms of where stigma presents, three main themes emerged - (1) through unpreparedness in dealing with patients with mental health conditions, (2) noticing mentors expressing stigma and (3) through the culture of medicine. The primary theme related to 'how best to support students to prevent negative attitudes from forming' was building stigma resistance to reduce the likelihood of perpetuating stigma towards patients with mental health conditions and therefore enhance patient care. The participants suggest six primary techniques to build stigma resistance, including (1) reflection, (2) skills building, (3) patient experiences, (4) examples and exemplars, (5) clinical application and (6) transforming structural barriers. We suggest these techniques combine to form the ReSPECT model for stigma resistance in the curriculum. CONCLUSIONS: The ReSPECT model derived from our research could provide a blueprint for medical educators to integrate stigma resistance throughout the curriculum from year one to better equip medical students with the potential to reduce interpersonal stigma and perhaps self-stigma. Ultimately, building stigma resistance could enhance care towards patients with mental health conditions and hopefully improve patient outcomes.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".