Preventing sexually transmitted infections among adolescents: an assessment of ecological approaches and study methods
Bibliographic record
Abstract
Most primary prevention research has attempted to explain sexual health outcomes, such as sexually transmitted infections, by focusing on individual characteristics (e.g. age), qualities (e.g. knowledge levels), and risk behaviour (e.g. unprotected intercourse). Emerging evidence indicates that population‐level health outcomes are unlikely to be explained adequately as an aggregate of such individual‐level factors. Rather, approaches that move beyond individualistic frameworks and adopt more ecological approaches may hold promise for promoting sexual health at the population level. This paper assessed the degree to which ecological approaches were integrated into empirical research regarding interventions to prevent sexually transmitted infections among adolescents. The paper also assessed the scientific rigour of the 35 intervention reports included in this review. Most (n = 31) reports focused exclusively on the micro‐level (e.g. individual knowledge and attitudes) issues. No studies accounted for macro‐level concerns (e.g. socio‐cultural influences). Three reports were rated as methodologically ‘strong,’ 11 were of moderate quality and 21 reports were rated as ‘weak.’ Most sexual health interventions targeting adolescents have focused nearly exclusively on individual risk, but have failed to yield encouraging results in terms of behaviour change or reducing disease burden in this population. More attention should be paid to ecological approaches and new study methods should be explored.
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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 | 0.470 | 0.350 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".