The effect of very preterm birth on the Five-Factor Model of personality traits: A meta-analysis of individual participant data
Bibliographic record
Abstract
There is mixed evidence on personality differences among those born very preterm or with very low birth weight (VP/VLBW). This meta-analysis of individual participant data aimed to examine differences in personality traits between VP/VLBW ( n = 568) and term-born ( n = 1,060) adults, and the role of neonatal characteristics and neurosensory impairments in childhood, which have not been previously investigated. Six studies were identified from two research consortia and a systematic search of the literature (PubMed and Scopus); studies were eligible if they included VP/VLBW and term-born adults followed from birth and assessed personality using the Five-Factor Model. Risk of bias (Newcastle-Ottawa Scale) was generally not a concern apart from the use of self-reported measures and the rate of follow-up. Using a one-stage approach, VP/VLBW scored lower on extraversion and openness and higher on neuroticism and agreeableness than term-born participants after adjusting for sex and parental education. Within the VP/VLBW group, those with bronchopulmonary dysplasia scored lower on extraversion and higher on neuroticism, with similar findings after removing participants with neurosensory impairments. Altogether, these findings suggest that a proportion of the effect of VP/VLBW birth on personality may be attributed to neonatal morbidities and altered brain development, although other confounding factors require further research.
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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 | 0.024 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.054 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".