A grounded theory model for reducing stigma in health professionals in Canada
Bibliographic record
Abstract
OBJECTIVE: The Mental Health Commission of Canada was formed as a national catalyst for improving the mental health system. One of its initiatives is Opening Minds (OM), whose mandate is to reduce mental health-related stigma. This article reports findings from a qualitative study on antistigma interventions for healthcare providers, which includes a process model articulating key stages and strategies for implementing successful antistigma programmes. METHOD: The study employed a grounded theory methodology. Data collection involved in-depth interviews with programme stakeholders, direct observation of programmes, a review of programme documents, and qualitative feedback from programme participants. Analysis proceeded via the constant comparison method. A model was generated to visually present key findings. RESULTS: Twenty-three in-depth interviews were conducted representing 18 different programmes. Eight programmes were observed directly, 48 programme documents were reviewed, and data from 1812 programme participants were reviewed. The analysis led to a four-stage process model for implementing successful antistigma programmes targeting healthcare providers, informed by the basic social process 'targeting the roots of healthcare provider stigma'. CONCLUSION: The process model developed through this research may function as a tool to help guide the development and implementation of antistigma programmes in healthcare contexts.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".