A systematic review of associations between substance use and sexual risk behaviour, stis and unplanned pregnancy in women
Bibliographic record
Abstract
Background/introduction: Associations between substance use and sexual risk among general populations of women may be helpful in the development of a sexual risk assessment tool for community health settings. Aim(s)/objectives: To review the evidence for whether smoking, alcohol and drug use variables are associated with reporting of unprotected sexual intercourse, multiple partnerships, STI diagnoses and unplanned pregnancy in women aged 16-44 years. Methods: Seven electronic databases were searched for probability population surveys published between 31/1/1994 and 31/1/2014 that reported on at least one of the outcomes above. Studies were included on women aged 16-44 years in the European Union, Australia, New Zealand, USA or Canada. An independent reviewer screened 10% of title and abstract exclusions and all full-text papers. Results: Three papers were identified. Current smoking was associated with unplanned pregnancy in the last year (Wellings 2013) and with current non-use of contraception among women (Xaverius 2009). Reporting ever smoking daily was also associated with reporting larger numbers of lifetime sexual partners (Cavazos-Rehg, 2011). Drug use in the last year (excepting cannabis) was associated with unplanned pregnancy (Wellings 2013). Cavazos-Rehg, 2011 found a dose response between lifetime partner numbers and heaviness of marijuana and alcohol use. Conversely Xaverius, 2009 found alcohol use was lower among those reporting current non-use of contraception. Discussion/conclusion:No clear direction emerged for the association with alcohol use, in contrast to drug use and smoking. Further research is needed to establish if alcohol has utility in a women's sexual risk assessment tool for community use.
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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 | 0.008 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.015 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".