Impact of Statistical Adjustment for Frequency of Venue Attendance in a Venue-based Survey of Men Who Have Sex With Men
Bibliographic record
Abstract
Venue sampling is a common sampling method for populations of men who have sex with men (MSM); however, men who visit venues frequently are more likely to be recruited. While statistical adjustment methods are recommended, these have received scant attention in the literature. We developed a novel approach to adjust for frequency of venue attendance (FVA) and assess the impact of associated bias in the ManCount Study, a venue-based survey of MSM conducted in Vancouver, British Columbia, Canada, in 2008-2009 to measure the prevalence of human immunodeficiency virus and other infections and associated behaviors. Sampling weights were determined from an abbreviated list of questions on venue attendance and were used to adjust estimates of prevalence for health and behavioral indicators using a Bayesian, model-based approach. We found little effect of FVA adjustment on biological or sexual behavior indicators (primary outcomes); however, adjustment for FVA did result in differences in the prevalence of demographic indicators, testing behaviors, and a small number of additional variables. While these findings are reassuring and lend credence to unadjusted prevalence estimates from this venue-based survey, adjustment for FVA did shed important insights on MSM subpopulations that were not well represented in the sample.
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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 | 0.177 | 0.428 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".