Informal confidential voting interviewing in a sexual risk assessment of men who have sex with men (MSM) and transgenders (hijra) in Bangalore, India
Bibliographic record
Abstract
OBJECTIVE: The accuracy of self-reporting sensitive sexual risk behaviours is highly susceptible to misreporting. Informal confidential voting interviews (ICVIs) may minimise social desirability bias by increasing the privacy of the interview setting. The objective was to investigate determinants of risky behaviour among men who have sex with men (MSM) and 'hijra' (transgenders) reported through two interviewing tools: ICVIs and face-to-face interviews (FTFIs). METHODS: Cluster random sampling was used to recruit MSM in 85 cruising sites in Bangalore, including eight hammams (bath houses) and 77 public locations where MSM and hijra cruise for sex. Individuals were randomly allocated to one of the data collection methods(5:2 FTFI : ICVI). Data were analysed using standard regression and a profile regression approach that associates clusters of behaviours with our outcome (FTFI vs ICVI). RESULTS: A total of 372 MSM and hijra were interviewed for the FTFIs and 153 respondents completed ICVIs. Participants were more likely to report injecting drug use (4% vs 1%; p=0.008) and paying to have sex with a female sex worker (FSW) in the last year (28% vs 8%; p=0.001) in the ICVIs. There were no differences to questions on sociodemographics, sexual debut with another male, non-condom use (12% vs 14%), ever selling sex to men (58% vs 56%), current female partner (26% vs 20%) and non-condom use with a main female partner (17% vs 19%). CONCLUSIONS: The significant differences between interview modes for certain outcomes, such as intravenous drug use and sex with a FSW, demonstrate how certain behaviour is stigmatised among the MSM community. Nevertheless, the lack of effect of the interviewing tool in other outcomes may indicate either less reporting bias in reporting this behaviour or environmental factors such as the interviewers not adequately screening themselves from the respondent or a potential disadvantage of using other MSM as interviewers.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".