Estimating the population size of gay, bisexual, and other men who have sex with men in four major provinces in Canada: A descriptive study using data from a population-based survey
Bibliographic record
Abstract
BACKGROUND: Estimating the size of key populations is critical for effective research and policy development. We estimated the population size of gay, bisexual, and other men who have sex with men (GBM) based on different definitions and compared the demographic composition of the GBM and non-GBM populations in Canada. METHODS: This descriptive study used data from the 2015-2016 and 2019-2020 Canadian Community Health Survey (CCHS) cycles. We selected men aged 18-64 years who had valid responses to the sexual identity and sexual behaviour contents. We explored different combinations of the survey questions to estimate the size of the GBM population in Canada and conducted a separate analysis for Canada's four most populous provinces, comparing sociodemographic characteristics. RESULTS: Using a definition of GBM combining sexual identity and behaviour (i.e., men who identify as gay or bisexual or who had sex with men in the last 12 months), the weighted proportion of GBM in the 2015-2016 cycle was 2.7% (95% Confidence Interval (CI) 1.9%-3.4%) in Alberta, 3.5% (95% CI 2.7%-4.4%) in British Columbia, 4.1% (95% CI 3.2%-4.9%) in Ontario, and 4.8% (95% CI 4.0%-5.7%) in Quebec. In the 2019-2020 cycle, the weighted proportion of GBM (i.e., men who identify as gay, bisexual or pansexual, or who had sex with men in the last 12 months) was 4.4% (95% CI 3.3%-5.4%) in British Columbia and 4.7% (95% CI 3.9%-5.4%) in Ontario. Overall, compared to non-GBM, GBM were more likely to be single/never married, have an annual household income of less than $30,000, live in medium and large population centres and have lower mean age. CONCLUSION: Our estimates showed sexual orientation discordance in Canada. Our findings also suggested that the GBM population might be increasing over time.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".