National trends in sexual health indicators among gay and bisexual men disaggregated by ethnicity: repeated cross-sectional behavioural surveillance in New Zealand
Bibliographic record
Abstract
OBJECTIVES: To assess trends in sexual health outcomes among men who have sex with men (MSM) disaggregated by ethnicity. DESIGN: Repeated cross-sectional. SETTING: Behavioural surveillance data from 2006, 2008, 2011 and 2014 were collected in-person and online across Aotearoa New Zealand. PARTICIPANTS: Eligible participants were self-identified men aged 16 years or older who reported sex with another man in the past 5 years. We classified 10 525 participants' ethnicities: Asian (n=1003, 9.8%), Māori (Indigenous people of Aotearoa New Zealand, n=1058, 10.3%), Pacific (n=424, 4.1%) and European (n=7867, 76.8%). OUTCOME MEASURES: The sexual health outcomes examined were >20 recent (past 6 months) male sexual partners, past-year sexually transmitted infection (STI) testing, past-year STI diagnosis, lifetime and past-year HIV testing, lifetime HIV-positive diagnosis and any recent (past 6 months) condomless anal intercourse with casual or regular partners. RESULTS: When disaggregated, Indigenous and ethnic minority groups reported sexual health trends that diverged from the European MSM and each other. For example, Asian MSM increased lifetime HIV testing (adjusted OR, AOR=1.31 per survey cycle, 95% CI 1.17 to 1.47) and recent HIV testing (AOR=1.14, 95% CI 1.02 to 1.28) with no changes among Māori MSM or Pacific MSM. Condomless anal intercourse with casual partners increased among Māori MSM (AOR=1.13, 95% CI 1.01 to 1.28) with no changes for Asian or Pacific MSM. Condomless anal intercourse with regular partners decreased among Pacific MSM (AOR=0.83, 95% CI 0.69 to 0.99) with no changes for Asian or Māori MSM. CONCLUSIONS: Population-level trends were driven by European MSM, masking important differences for Indigenous and ethnic minority sub-groups. Surveillance data disaggregated by ethnicity highlight inequities in sexual health service access and prevention uptake. Future research should collect, analyse and report disaggregated data by ethnicity to advance health equity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".