Cognitive and Mental Health of Young Mothers’ Offspring: A Meta-analysis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: The nature and magnitude of the cognitive and mental health risks among the offspring of young mothers is not fully understood. Our objective is to examine the risk of mental disorders in these offspring. METHODS: Five databases (Medline, Embase, Web of Science, PsycINFO, and CINAHL) were searched from their inceptions until February 2022. Studies were eligible if they assessed offspring of young mothers (<21 years), contained a control group, and assessed any cognitive and/or mental health outcomes. Random-effects meta-analysis was used to generate standardized mean differences (SMDs) in infants (0-3 years), children (4-9), adolescents (10-19), and adults (20+). Methodological bias was assessed using the Newcastle-Ottawa Scale. RESULTS: 51 outcomes were meta-analyzed. Levels of cognitive and learning problems were higher among the infants (SMD = 0.30 [95% confidence interval 0.0-0.55]) and adolescents (SMD = 0.43 [0.24 to 0.62]) of young mothers. Adolescents had more symptoms of delinquency (SMD = 0.24 [0.12 to 0.36]). As adults, they are more often convicted of violent crimes (SMD = 0.36 [0.22 to 0.50]). Internalizing symptoms were higher in these offspring in childhood (SMD = 0.29 [0.14 to 0.45]) and adulthood (SMD = 0.35 [0.34 to 0.36]). This review uses unadjusted data and is thus unequipped to infer causality. Studies have high attrition and rely heavily on self-report. CONCLUSIONS: Young mothers' offspring have more cognitive, externalizing, and internalizing problems across the lifespan than individuals born to mothers ≥21 years of age. They may benefit from early detection and support.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.041 |
| Bibliometrics | 0.004 | 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".