Factors associated with recent HIV testing among younger gay and bisexual men in New Zealand, 2006-2011
Bibliographic record
Abstract
BACKGROUND: Understanding HIV testing behaviour is vital to developing evidence-based policy and programming that supports optimal HIV care, support, and prevention. This has not been investigated among younger gay, bisexual, and other men who have sex with men (YMSM, aged 16-29) in New Zealand. METHODS: National HIV sociobehavioural surveillance data from 2006, 2008, and 2011 was pooled to determine the prevalence of recent HIV testing (in the last 12 months) among YMSM. Factors associated with recent testing were determined using manual backward stepwise multivariate logistic regression. RESULTS: Of 3,352 eligible YMSM, 1,338 (39.9%) reported a recent HIV test. In the final adjusted model, the odds of having a recent HIV test were higher for YMSM who were older, spent more time with other gay men, reported multiple sex partners, had a regular partner for 6-12 months, reported high condom use with casual partners, and disagreed that HIV is a less serious threat nowadays and that an HIV-positive man would disclose before sex. The odds of having a recent HIV test were lower for YMSM who were bisexual, recruited online, reported Pacific Islander or Asian ethnicities, reported no regular partner or one for >3 years, were insertive-only during anal intercourse with a regular partner, and who had less HIV-related knowledge. CONCLUSION: A priority for HIV management should be connecting YMSM at risk of infection, but unlikely to test with appropriate testing services. New generations of YMSM require targeted, culturally relevant health promotion that provides accurate understandings about HIV transmission and prevention.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".