Knowing so much, yet knowing so little: a scoping review of interventions that address the stigma of mental illness in the Canadian context
Bibliographic record
Abstract
Stigma can have detrimental effects on the health and wellbeing of individuals living with a mental illness. This scoping review describes the nature, range, and extent of intervention research aimed at reducing public and self-stigma of mental illness in the Canadian context. The review was guided by Arksey and O'Malley's framework. A search of databases and relevant websites identified 35 primary studies. Most studies used quantitative research methods and included predominantly youth or middle-aged adults, women, and white Canadian-born people. Guided by different conceptualizations of stigma, direct or indirect contact, education, and advocacy-focused interventions, aimed to provide information, and/or develop skills to address self and public stigma. Most studies evaluated interventions' effectiveness short-term. Of the few studies that followed-up participants long-term, some were able to reduce stigmatizing attitudes post-intervention, however, these targeted only specific groups such as students or health care professionals. Lack of diversity among the samples, and limited evidence of long-term effectiveness of interventions, were some of the studies' limitations. What is currently known about interventions aimed at reducing the stigma of mental illness in the Canadian context is not informed by research among vulnerable groups, such as people living with a mental illness, older adults, immigrants, and people of diverse ethnic backgrounds. Interventions that are informed by clear conceptualizations of stigma and rigorously evaluated in a range of ethno-cultural groups would create a knowledge base that is useful for policy-makers, community leaders, and agencies serving various ethnic communities in Canada.
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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 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.023 | 0.031 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".