An analysis of socio-demographic and behavioural factors among immigrant MSM in Montreal from an HIV-testing site sample
Bibliographic record
Abstract
Little Canadian research exists on immigrant men who have sex with men (MSM), who are internationally reported to use rapid HIV-testing sites. Our objective was to describe immigrant MSM in a sample of clients at an HIV testing site. From July 2012 to November 2013, clients at Actuel sur Rue (AsR), a Montreal-based HIV rapid-testing site, provided data for a staff- and a self-administered questionnaire. We compared immigrant and non-immigrant MSM's socio-demographics and risk practices. Among immigrants, we analyzed these variables by country of origin. We conducted regression analyses examining how immigrant status and socio-demographics were associated with risk practices. During the study, 1353 MSM visited AsR and 407 (30%) were immigrants, mostly from Europe, Latin America/Caribbean, and Africa/Middle-East. The same proportion (2%) of immigrant and non-immigrant MSM received a positive rapid HIV test result. Relative to non-immigrant MSM, significantly more immigrant MSM reported a post-secondary degree, a lower income, and being unemployed. Fewer reported receiving an HIV-positive/unknown-status partner's sperm/blood in their mouth, ever having unprotected sex with an HIV-positive partner, and ever selling sex. In comparisons between MSM immigrants by origin, fewer Asian and African/Middle-Eastern MSM reported ever testing for HIV. In the regression analyses, immigrant status was not independently associated with sexual risk. MSM who earned less, were unemployed, or had a high school degree or less were more likely to have sold sex. Socio-demographics like employment, education and income were associated with reported sexual risk in MSM clients, but not immigrant status alone.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".