Population-based methods for estimating the number of men who have sex with men: a systematic review
Bibliographic record
Abstract
The objective of this systematic review was to summarise population-based methods (i.e. methods that used representative data from populations) for estimating the population size of men who have sex with men (MSM), a high-risk group for HIV and other sexually transmissible infections (STIs). Studies using population-based methods to estimate the number or percentage of MSM or gay men were included. Twenty-eight studies met the inclusion criteria. Seven studies used surveillance data, 18 studies used survey data, and six studies used census data. Sixteen studies were conducted in the US, five were conducted in European countries, two were conducted in Canada, three were conducted in Australia, one was conducted in Israel, and one was conducted in Kenya. MSM accounted for 0.03-6.5% of men among all studies, and ranged from 3.8% to 6.4% in the US, from 7000 to 39100 in Canada, from 0.03% to 6.5% in European countries, and from 127947 to 182624 in Australia. Studies using surveillance data obtained the highest estimates of the MSM population size, whereas those using survey data obtained the lowest estimates. Studies also estimated the MSM population size by dimensions of sexual orientation. In studies examining these dimensions, fewer people identified as MSM than reported experience with or attraction to other men. Selection bias, differences in recall periods and sampling, or stigma could affect the estimate. It is important to have an estimate of the number of MSM to calculate disease rates, plan HIV and STI prevention efforts, and to allocate resources for this group.
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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 | 0.034 | 0.165 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.029 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".