Implications of the stigma of mental illness for professional knowledge development and practice: An Interprofessional Health Education framework from structural violence perspectives
Bibliographic record
Abstract
Abstract Persons with mental illness (PWMI) continue to encounter stigma from the public with negative outcomes. Recent stigma discourse points to power differentials as key in shaping stigma related to mental illness within social settings. The perceived social injustice towards PWMI is known to exist both anecdotally and in documented discourses. Stigma constitutes the product of public attitudes and behaviors that characterize labeling, stereotyping, prejudice, cognitive separation, status loss, and discrimination that lead to responses that may include stress and esteem‐related appraisal of experienced, anticipated, perceived, or personal endorsement of societal actions that are anchored by existing power relational differentials. The potential consequence of such societal injustices (unfair treatments) towards PWMI may result in stigma and its sequels, including low socioeconomic status, stress, low self‐esteem, unemployment, homelessness, exclusion, and human rights abuse. This paper proposes an Interprofessional Health Education framework and discusses the implications of such unfair social treatments for Professional knowledge development and practice among healthcare professionals, with the view to improving collaboration and patient care outcomes. A more collaborative model of care, where service users and clinicians regard each other as knowledgeable with shared power to achieve healthy outcomes, empowers patients even more in areas where they fall short.
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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 | 0.020 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.016 | 0.058 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.003 | 0.020 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".