Self-Reported Mental Health Problems Among Adults Born Preterm: A Meta-analysis
Bibliographic record
Abstract
CONTEXT: Preterm birth increases the risk for mental disorders in adulthood, yet findings on self-reported or subclinical mental health problems are mixed. OBJECTIVE: To study self-reported mental health problems among adults born preterm at very low birth weight (VLBW; ≤1500 g) compared with term controls in an individual participant data meta-analysis. DATA SOURCES: Adults Born Preterm International Collaboration. STUDY SELECTION: = 1512). DATA EXTRACTION: We obtained individual participant data from 6 study cohorts and compared preterm and control groups by mixed random coefficient linear and Tobit regression. RESULTS: Adults born preterm reported more internalizing (pooled β = .06; 95% confidence interval .01 to .11) and avoidant personality problems (.11; .05 to .17), and less externalizing (-.10; -.15 to -.06), rule breaking (-.10; -.15 to -.05), intrusive behavior (-.14; -.19 to -.09), and antisocial personality problems (-.09; -.14 to -.04) than controls. Group differences did not systematically vary by sex, intrauterine growth pattern, neurosensory impairments, or study cohort. LIMITATIONS: Exclusively self-reported data are not confirmed by alternative data sources. CONCLUSIONS: Self-reports of adults born preterm at VLBW reveal a heightened risk for internalizing problems and socially avoidant personality traits together with a lowered risk for externalizing problem types. Our findings support the view that preterm birth constitutes an early vulnerability factor with long-term consequences on the individual into adulthood.
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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 | 0.012 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.035 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".