The Association Between Self-Reported Depressive Symptoms and Risky Sexual Interactions in an Injection Drug Using Population in Winnipeg, Canada
Bibliographic record
Abstract
Background: Vulnerable populations in Canada shoulder a disproportionately high burden of disease. Transmission of sexually transmitted infections is behaviourally mediated. Previous research indicates an association between depression and sexual risk-taking. Evidence also suggests that social support is an effect modifier. Methods: Data were collected from a population of injection drug users, between 2003 and 2004 in Winnipeg, using respondent driven sampling. Demographic and social behaviors were analyzed to characterize the population, as well as social networks and ego networks. Logistic regression was used to examine the association between depressive symptoms and sexual risk interactions. Social support was examined as an effect modifier. Results: The majority of the study participants and network members were aged between 35 and 44, and a high percentage identified as Native Canadians. The highest percentage of people reported welfare as their primary source of income, and injecting stimulants, as their most frequently injected drug. Logistic regression models indicated an increase in the odds of individuals engaging in high-risk sexual interactions, if they had also self-reported elevated depressive symptoms. It was not possible to conclude that social support was an effect modifier. Conclusion: This research supports a positive association between elevated depressive symptoms, and higher levels of sexual risk interactions. Further research is needed to understand the role of social support.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".