Normalising disclosure or reinforcing heroism? An exploratory critical discourse analysis of mental health stigma in medical education
Bibliographic record
Abstract
INTRODUCTION: There has been a proliferation of initiatives targeted towards improving psychological wellbeing among medical learners. Yet many learners do not seek assistance due to stigma against help seeking. Understanding the prevailing discourses on the effects of mental health stigma in the context of medical education will improve insight on how to address stigma and improve wellbeing. In this study, the authors sought to explore discourses on stigma in medical education through a Foucauldian Critical Discourse Analysis. METHODS: The authors assembled several sets of texts related to stigma in medical education. The initial archive consisted of social media discourse and was expanded to include digital news media. Next, the authors conducted semi-structured qualitative interviews with medical students, residents and faculty. Using principles of Critical Discourse Analysis informed by the writings of Michel Foucault, the authors analysed the archive to identify truth statements, representative statements and discursive effects. RESULTS: Analysis revealed an emancipatory discourse of disclosure that normalised help-seeking, which conflicted with a discourse of performance. Results suggested that public disclosure remains challenging in private contexts due to a medical culture that rewards perfectionism and lauds heroism. Discourses on performance positioned disclosure as disruptive to the system's need to maintain its own hegemony. Overall, stigma was perceived as rooted within the structural power of the medical education system and society at large. CONCLUSION: Discourses on stigma in medical education hold implications for the teaching, learning and overall wellbeing of medical learners. The tensions between discourses on disclosure and performance have the potential to perpetuate further distress for learners and worsen asymmetries in power. Interventions to address stigma would benefit from understanding and addressing the role of power and hierarchy in maintaining and dismantling stigma.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.014 | 0.027 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".