Correlates of unprotected sex by client type among female sex workers that inject drugs in Tijuana and Ciudad Juarez, Mexico
Bibliographic record
Abstract
Lindsay, R., Roesch, S., Strathdee, S., Rangel, M., Staines-Orozco, H., Abramovitz, D., Ulibarri, M., & Rusch, M. (2015). Correlates of unprotected sex by client type among female sex workers that inject drugs in Tijuana and Ciudad Juarez, Mexico. The International Journal Of Alcohol And Drug Research, 4(2), 159-169. doi:http://dx.doi.org/10.7895/ijadr.v4i2.208Aims: Risk environment factors may influence unprotected sex between female sex workers who are also injection drug users(FSW-IDUs) and their regular and non-regular clients differently. Our objective is to identify correlates of unprotected vaginalsex in the context of client type.Methods: A cross-sectional survey of 583 FSW-IDUs in Tijuana and Ciudad Juarez, Mexico, was analyzed using negativebinomial regression to determine physical, social, economic, and policy risk-environment factors that affect the frequency ofunprotected sex with regular and non-regular clients.Results: Median number of unprotected vaginal sex acts in the past month among FSW-IDUs and their regular and non-regularclients was 11 (IQR 3–30) and 13 (IQR 5–30), respectively. Correlates differed by site and client type and were most closelyassociated with the risk environment. In Tijuana, social factors (e.g., injecting drugs with clients) were independently associatedwith more unprotected sex. Factors independently associated with less unprotected sex across client type and site included socialand economic risk environment factors (e.g., receiving more money for unprotected sex). In the policy risk environment, alwayshaving free access to condoms was independently associated with less unprotected sex among non-regular clients in Tijuana(Risk rate ratio = 0.64; 95% confidence interval 0.43–0.97).Conclusions: Primarily physical, social, and economic risk-environment factors were associated with unprotected vaginal sexbetween FSW-IDUs and both client types, suggesting potential avenues for intervention.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".