An Educational Framework for Healthcare Ethics Consultation to Approach Structural Stigma in Mental Health and Substance Use Health
Bibliographic record
Abstract
This paper addresses the need for, and ultimately proposes, an educational framework to develop competencies in attending to ethical issues in mental health and substance use health (MHSUH) in healthcare ethics consultation (HCEC). Given the prevalence and stigma associated with MHSUH, it is crucial for healthcare ethicists to approach such matters skillfully. A literature review was conducted in the areas of bioethics, health professions education, and stigma studies, followed by quality improvement interviews with content experts to gather feedback on the framework's strengths, limitations, and anticipated utility. The proposed framework describes three key concepts: first, integrating self-reflexive practices into formal, informal, and hidden curricula; second, embedding structural humility into teaching methods and contexts of learning; and third, striking a balance between critical consciousness and compassion in dialogue. The proposed educational framework has the potential to help HCEC learners enhance their understanding and awareness of ethical issues related to structural stigma and MHSUH. Moreover, context-specific learning, particularly in MHSUH, can play a significant role in promoting competency-building among healthcare ethicists, allowing them to address issues of social justice effectively in their practice. Further dialogue is encouraged within the healthcare ethics community to further develop the concepts described in this framework.
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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 | 0.023 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".