“You have to make it cool”: How heterosexual Black men in Toronto, Canada, conceptualize policy and programs to address HIV and promote health
Bibliographic record
Abstract
BACKGROUND: Black Canadian communities are disproportionately impacted by HIV. To help address this challenge, we undertook research to engage heterosexual Black men in critical dialogue about resilience and vulnerability. They articulated the necessity of making health services 'cool'. METHODS: We draw on the analyses of focus groups and in-depth interviews with 69 self-identified heterosexual Black men and 12 service providers who took part in the 2016 Toronto arm of the weSpeak study to explore what it means to make health and HIV services 'cool' for heterosexual Black Canadian men. RESULTS: Our findings revealed four themes on making health services cool: (1) health promotion as a function of Black family systems; (2) opportunities for healthy dialogue among peers through non-judgmental interactions; (3) partnering Black men in intervention design; and (4) strengthening institutional health literacy on Black men's health. CONCLUSIONS: We discuss the implications of these findings for improving the health of Black Canadians.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.036 | 0.015 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 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".