Risk Indicators of Depressed Mood among Sex-Trade Workers and Implications for HIV Risk Behaviour
Bibliographic record
Abstract
OBJECTIVES: To determine the prevalence of depressed mood among people who have traded sex for money in the Saskatoon Health Region (SHR), the adjusted risk factors for depressed mood among this sample, and if depressed mood was associated with decreased self-efficacy for safe sexual practices and injection drug use. METHODS: Two-hundred ninety-nine people who have traded sex for money were surveyed with validated instruments for measuring risk behaviours, depressed mood, and self-efficacy for safe sexual practices. RESULTS: The sample consisted primarily of low-income, poorly educated Aboriginal women, many of whom also indicated using injection drugs. Using the 16-point score cut-off for the Center for Epidemiologic Studies Depression Scale, 84.6% of participants had depressed mood. When the cut-off score was 23 points or higher, 65.9% had depressed mood. After multivariate analysis, covariates that had an independent association with depressed mood included injecting a drug in the past 4 weeks (OR 1.59; 95% CI 1.2 to 1.8), suffering the death or permanent separation from a parent before the age of 18 (OR 2.09; 95% CI 1.05 to 4.15), and physical assault or abuse by a partner in adult life (OR 2.79; 95% CI 1.38 to 5.64). Depressed mood was associated with lower self-efficacy scores for safe sexual behaviours. CONCLUSIONS: Our study suggests that high rates of depressed mood among people who have traded sex for money is associated with injection drug use and low self-efficacy for safe sexual health practices. These findings are important and may help explain the high rates of human immunodeficiency virus within the SHR.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".