Dismantling Structural Stigma Related to Mental Health and Substance Use: An Educational Framework
Bibliographic record
Abstract
Stigma related to mental health and substance use (MHSU) is a well-established construct that describes how inequitable health outcomes can result from prejudice, discrimination, and marginalization. Although there is a body of literature on educational approaches to reduce stigma, antistigma education for MHSU has primarily focused on stigma at the social, interpersonal/public, and personal (self-stigma) levels, with little attention to the problem of structural stigma. Structural stigma refers to how inequity is manifested through rules, policies, and procedures embedded within organizations and society at large. Structural stigma is also prominent within clinical learning environments and can be transmitted through role modeling, resulting in inequitable treatment of vulnerable patient populations. Addressing structural stigma through education, therefore, has the potential to improve equity and enhance care. A promising educational approach for addressing structural stigma is structural competency, which aims to enhance health professionals' ability to recognize and respond to social and structural determinants that produce or maintain health disparities. In this article, the authors propose a framework for addressing structural MHSU stigma in health professions education that has 4 key components and is rooted in structural humility: recognizing structural forms of stigma; reflecting critically on one's own assumptions, values, and biases; reframing language away from stereotyping toward empathic terms; and responding with actions that actively dismantle structural MHSU stigma. The authors propose evidence-informed and practical suggestions on how structural competency may be applied within clinical learning environments to dismantle structural MHSU stigma in organizations and society at large.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".