Depression and sexual risk behaviours among people who inject drugs: a gender-based analysis
Bibliographic record
Abstract
UNLABELLED: Background Although many people who inject drugs (PID) contend with comorbidities, including high rates of mental illness, limited attention has been given to the differences in comorbidities among men and women or the potential links between psychiatric disorders and HIV risk behaviours. We sought to longitudinally examine associations between depression and HIV-related sexual risk behaviours among PID, stratified by gender. METHODS: Data were derived from a prospective cohort of PID in Vancouver, Canada between December 2005 and November 2009. Using generalised estimating equations, we examined the relationship between depressive symptoms and two types of sexual HIV risk behaviours: engaging in unprotected sex; and having multiple sexual partners. All analyses were stratified by self-reported gender. RESULTS: Overall, 1017 PID participated in this study, including 331 (32.5%) women. At baseline, women reported significantly higher depressive symptoms than men (P<0.001). In multivariate generalised estimating equations analyses, after adjustment for potential social, demographic and behavioural confounders, more severe depressive symptomology remained independently associated with engaging in unprotected sex [adjusted odds ratio (AOR)=1.62, 95% confidence interval (CI): 1.18-2.23] and having multiple sexual partners (AOR=1.54, 95% CI: 1.09-2.19) among women, but was only marginally associated with having multiple sexual partners among men (AOR=1.18, 95% CI: 0.98-1.41). CONCLUSIONS: These findings call for improved integration of psychiatric screening and treatment services within existing public health initiatives designed for PID, particularly for women. Efforts are also needed to address sexual risk-taking among female PID contending with clinically significant depression.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".