Stigma and discrimination related to mental health and substance use issues in primary health care in Toronto, Canada: a qualitative study
Bibliographic record
Abstract
Purpose: Community Health Centres (CHCs) are an essential component of primary health care (PHC) in Canada. This article examines health providers’ understandings and experiences regarding stigma towards mental health and substance use (MHSU) issues, as well as their ideas for an effective intervention to address stigma and discrimination, in three CHCs in Toronto, Ontario. Methods: Using a phenomenological approach, we conducted twenty-three interviews with senior staff members and peer workers, and three focus groups with front-line health providers. Ahybrid approach to thematic analysis was employed, entailing a combination of emergent and a priori coding. Results: The findings indicate that PHC settings are sites where multiple forms of stigma create health service barriers. Stigma and discrimination associated with MHSU also cohere around intersecting experiences of gender, race, class, age and other issues including the degree and visibility of distress. Clients may find social norms to be alienating, including behavioural expectations in Canadian PHC settings. Conclusions: Given the turmoil in clients’ lives, systematic efforts to mitigate stigma were inhibited by myriad proximate factors that demanded urgent response. Health providers were enthusiastic about implementing anti-stigma/recovery-based approaches that could be integrated into current CHC services. Their recommendations for interventions centred around communication and education, such as training, CHC-wide meetings, and anti-stigma campaigns in surrounding communities.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.023 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 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".