Role of Social Support in the Relationship between Sexually Transmitted Infection and Depression among Young Women in Canada
Bibliographic record
Abstract
BACKGROUND: Individuals with a self-reported history of sexually transmitted infection (STI) are at high risk for depression. However, little is known about how social support affects the association between STI and depression among young women in Canada. METHODS: Data were drawn from the Canadian Community Health Survey (CCHS), conducted in 2005. A total of 2636 women aged 15-24 years who provided information on STI history were included in the analysis. Depression was measured by a depression scale based on the Composite International Diagnostic Interview Short-Form (CIDI-SF). The 19-item Medical Outcomes Study (MOS) Social Support Survey assessed functional social support. A log-binomial model was used to estimate the prevalence ratio (PR) for self-reported STI history associated with depression and to assess the impact of social support on the association. RESULTS: The adjusted PR for self-reported STI history associated with depression was 1.61 (95% CI, 1.03 to 2.37), before social support was included in the model. The association between STI history and depression was no longer significant when social support was included in the model (adjusted PR, 1.28; 95% CI, 0.83 to 1.84). The adjusted PRs for depression among those with low and intermediate levels of social support versus those with a high level of social support were 5.62 (95% CI, 3.50 to 9.56) and 2.19 (1.38 to 3.68), respectively. CONCLUSIONS: Social support is an important determinant of depression and reduces the impact of self-reported STI on depression among young women in Canada.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".