Your blues ain't like mine: considering integrative antiracism in <scp>HIV</scp> prevention research with black men who have sex with men in <scp>C</scp>anada and the <scp>U</scp>nited <scp>S</scp>tates
Bibliographic record
Abstract
Evidence-based interventions have been developed and used to prevent HIV infections among black men who have sex with men (MSM) in Canada and the United States; however, the degree to which interventions address racism and other interlocking oppressions that influence HIV vulnerability is not well known. We utilize integrative antiracism to guide a review of HIV prevention intervention studies with black MSM and to determine how racism and religious oppression are addressed in the current intervention evidence base. We searched CINAHL, PsychInfo, MEDLINE and the CDC compendium of evidence-based HIV prevention interventions and identified seventeen interventions. Three interventions targeted black MSM, yet only one intervention addressed racism, religious oppression, cultural assets and religious assets. Most interventions' samples included low numbers of black MSM. More research is needed on interventions that address racism and religious oppression on HIV vulnerability among black MSM. Future research should focus on explicating mechanisms by which multiple oppressions impact HIV vulnerability. We recommend the development and integration of social justice tools for nursing practice that aid in addressing the impacts of racism and other oppressions on HIV vulnerability of black MSM.
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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 | 0.042 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.005 |
| 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".