Social determinants of health and adverse maternal and birth outcomes in adolescent pregnancies: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Adverse outcomes in adolescent pregnancies have been attributed to both biological immaturity and social determinants of health (SDOH). The present systematic review evaluated the evidence on the association between SDOH and adverse maternal and birth outcomes in adolescent mothers. METHODS: Comprehensive literature searches were conducted to identify observational studies evaluating the relationship between SDOH and adverse adolescent pregnancy outcomes. Study selection, risk of bias appraisal, and data extraction of study characteristics were independently performed by two reviewers. Pooled odds ratios (pOR) with 95% confidence intervals (95% CI) were calculated to assess the association between SDOH and adverse birth outcomes. RESULTS: Thirty-one studies met the inclusion criteria. The most frequently evaluated SDOH was race while the most commonly reported maternal and birth outcomes were caesarean section and preterm birth (PTB), respectively. The risk of bias of included studies was fair on the Newcastle-Ottawa Scale. Meta-analyses of retrospective cohort studies showed that, compared to White adolescent mothers, African American teens had increased odds of PTB (pOR 1.67; 95% CI 1.59, 1.75) and low birthweight (pOR 1.53; 95% CI 1.45, 1.62). Rural residence was consistently linked with PTB while low maternal socio-economic (SES) and illiteracy were found to increase the risk of adolescent maternal mortality and LBW infants. CONCLUSION: Social determinants of health contribute to the risk of adverse pregnancy outcomes in adolescent mothers. African American race, rural residence, inadequate education, and low SES are markers for poor pregnancy outcomes in adolescent mothers. Further research needs to be done to understand the underlying causal pathways to inequalities in adolescent pregnancy 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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".