Perinatal and early-life factors associated with stable and unstable trajectories of psychopathic traits across childhood
Bibliographic record
Abstract
Abstract Background This study aimed to identify perinatal and early-life factors associated with trajectories of psychopathic traits across childhood. Methods Participants were 1631 children (51.5% girls) from the Quebec Longitudinal Study of Child Development. A wide range of perinatal and early-life factors were assessed from pregnancy to age 2.5 years using medical files and mothers’ reports. Psychopathic traits were assessed via teachers’ reports at ages 6, 7, 8, 10, and 12 years. Latent class growth analyses and multinomial logistic regressions controlling for child sex were conducted. Two-way interaction effects between perinatal/early-life factors and child sex were explored. Results Four trajectories of psychopathic traits were identified: High-stable (4.48%), Increasing (8.77%), Decreasing (11.46%), and Low-stable (75.29%). A few perinatal factors and most child-level and family-level early-life factors significantly increased the odds of following the High-stablev.the Low-stable trajectory. Higher levels of psychotropic exposures during pregnancy, socioeconomic adversity, child's physical aggression, child's opposition, mother's depressive symptoms, and hostile parenting increased the likelihood of following the Increasing instead of the Low-stable trajectory. Higher socioeconomic adversity, mother's depressive symptoms, and inconsistent parenting were associated with membership to the High-stable instead of the Decreasing trajectory. Most associations were not moderated by child sex. Conclusions These results shed light on the perinatal and early-life factors that are associated with specific pathways of psychopathic traits during childhood and suggest that different factors could be targeted to prevent the exacerbation (v.low and stable levels) or the stability at high levels (v.attenuation) of these traits.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".