Addressing HIV vulnerability and promoting resilience among heterosexual Black men and communities in Ontario, Canada: A concept-mapping approach
Bibliographic record
Abstract
We used concept-mapping methods to gain insights into promising HIV prevention intervention strategies from the collective experience of heterosexual Black men (HBM) in four cities of Ontario. We engaged 60 HBM in online group concept-mapping sessions. First, we held a brainstorming session where the HBM generated 226 statements anonymously on strategies to reduce HIV vulnerabilities. The statements were condensed to 123. Second, the HBM ( n = 45) sorted the 123 statements into self-created categories. Third, the HBM rated the strategy statements by importance ( n = 45) and feasibility ( n = 41). Finally, cluster analysis and multidimensional scaling were used to describe data patterns. The statements cluster tagged “family and individual level interventions” had the highest mean rating in importance (4.061) and feasibility (3.610). The policy interventions cluster solution was rated second highest in importance (IR = 4.058) and the sixth in feasibility (FR = 3.413). Other cluster solutions ratings were addressing racism (third highest in importance [IR = 4.030] and fourth highest in feasibility [FR = 3.514]); healthcare, research, and economic opportunities (fourth highest in importance [IR = 4.018] and third highest in feasibility [FR = 3.526]); sexual health awareness strategies (fifth highest in importance [IR = 3.993] and highest in feasibility [FR = 3.611]) and sex/HIV education (sixth highest in importance [IR = 3.975] and fifth highest in feasibility [FR = 3.442]); and individual and community empowerment (lowest in importance [IR = 3.846] and lowest in feasibility [FR = 3.375]). Intergenerational (family and individual), policy, and anti-racism interventions are the top three priority strategies for HIV prevention and care for HBM and communities in Ontario.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.005 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".