Key Ingredients of Anti-Stigma Programs for Health Care Providers: A Data Synthesis of Evaluative Studies
Bibliographic record
Abstract
OBJECTIVE: As part of its ongoing effort to combat stigma against mental illness among health care providers, the Mental Health Commission of Canada partnered with organizations conducting anti-stigma interventions. Our objective was to evaluate program effectiveness and to better understand what makes some programs more effective than others. Our paper reports the elements of these programs found to be most strongly associated with favourable outcomes. METHODS: Our study employed a multi-phased, mixed-methods design. First, a grounded theory qualitative study was undertaken to identify key program elements. Next, each program (n = 22) was coded according to the presence or absence of the identified key program ingredients. Then, random-effects, meta-regression modelling was used to examine the association between program outcomes and the key ingredients. RESULTS: The qualitative analysis led to a 6-ingredient model of key program elements. Results of the quantitative analysis showed that programs that included all 6 of these ingredients performed significantly better than those that did not. Individual analyses of each of the 6 ingredients showed that including multiple forms of social contact and emphasizing recovery were characteristics of the most effective programs. CONCLUSIONS: The results provide a validation of a 6-ingredient model of key program elements for anti-stigma programming for health care providers. Emphasizing recovery and including multiple types of social contact are of particular importance for maximizing the effectiveness of anti-stigma programs for health care providers.
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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 | 0.128 | 0.291 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.016 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".