Fertility rates and perinatal outcomes of adolescent pregnancies: a retrospective population-based study
Bibliographic record
Abstract
Objective: analyze trends in fertility rates and associations with perinatal outcomes for adolescents in Santa Catarina, Brazil. Methods: a population-based study covering 2006 to 2013 was carried out to evaluate associations between perinatal outcomes and age groups, using odds ratios, and Chi-squared tests. Results: differences in the fertility rate among female adolescents across regions and time period were observed, ranging from 40.9 to 72.0 per 1,000 in mothers aged 15-19 years. Adolescents had fewer prenatal care appointments than mothers ≥20 years, and a higher proportion had no partner. Mothers aged 15-19 years were more likely to experience preterm birth (OR:1.1; CI:1.08-1.13; p<0.001), have an infant with low birthweight (OR:1.1; CI:1.10-1.15; p<0.001) and low Apgar score at 5 minutes (OR:1.4; CI:1.34-1.45; p<0.001) than mothers ≥20 years, with the odds for adverse outcomes greater for those aged 10-14 years. Conclusion: this study provides evidence of fertility rates among adolescents remaining higher in regions of social and economic deprivation. Adolescent mothers and their infants more likely to experience adverse perinatal outcomes. Nurses, public health practitioners, health and social care professionals and educators need to work collaboratively to better target strategies for adolescents at greater risk; to help reduce fertility rates and improve outcomes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".