Antecedents of teenage pregnancy from a 14-year follow-up study using data linkage
Bibliographic record
Abstract
\n \t\t\tBackground: Many western nations continue to have high rates of teenage pregnancies and births, which can result in adverse outcomes for both mother and child. This study identified possible antecedents of teenage pregnancy using linked data from administrative sources to create a 14-year follow-up from a cross-sectional survey.Methods: Data were drawn from two sources - the 1993 Western Australian Child Health Survey (WACHS), a population-based representative sample of 2,736 children aged 4 to 16 years (1,374 girls); and administrative data relating to all their subsequent births and hospital admissions. We used weighted population estimates to examine differences between rates for teenage pregnancy, motherhood and abortion. We used Cox proportional hazards regression to model risk for teenage pregnancy.Results: There were 155 girls aged less than 20 years at the time of their first recorded pregnancy. Teenage pregnancy was significantly associated with: family type; highest school year completed by primary carer; combined carer income; whether the primary carer was a smoker; and whether the girl herself displayed aggressive and delinquent behaviours. An age-interaction analysis on the association with aggressive and delinquent behaviours found that while girls with aggressive and delinquent behaviours who were older at the time of the survey were at highest risk of teenage pregnancy, there was elevated risk for future teenage pregnancy across all ages.Conclusions: Our findings suggest that interventions to reduce teenage pregnancy rates could be introduced during primary school years, including those that are focused on the prevention and management of aggressive and delinquent behaviour.\n
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".