Disentangling the developmental and conceptual links between emotion dysregulation, self-regulation and internalizing and externalizing difficulties in childhood: a longitudinal investigation
Bibliographic record
Abstract
There is a close association between emotion regulation and mental ill-health but how they influence each other over time is unclear. The close association between the constructs also raises the question of how conceptually distinct or similar they are. We use data from the UK Millennium Cohort Study to investigate temporal and conceptual relationships between emotion regulation and mental health difficulties in childhood.Data from 16,859 children were analysed. The analytic sample included 48.85% female and 51.15% male participants. Ethnic representation was 82.3% White, 3.04% Mixed, 9.4% Asian, and 3.6% as Black or Black British. Study 1 used a cross-lagged model to assess bi-directional effects and temporal sequencing between internalizing and externalizing symptoms and emotion regulation at ages 3, 5, 7 years. We found cascading effects across all ages, whereby emotion dysregulation predicted later mental health difficulties, and externalizing symptoms predicted later emotion dysregulation. Study 2 used a hierarchical bi-factor model, which demonstrated a substantial overlap between the constructs during childhood, with the bi-factor explaining between .52 and .58 of the variance. We also tested the predictive utility of the included factors, of which the bifactor was the best predictor of self-harm and depression symptoms at age 14. Our findings demonstrate a significant overlap between emotion regulation and mental ill-health, which are intrinsically linked with no clear indication of which comes first. Further investigation of this relationship with more comprehensive measures is necessary to reliably inform intervention and prevention efforts.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".