Longitudinal associations between mother–child conflict and child internalizing problems in mid-childhood
Bibliographic record
Abstract
Several aspects of mother-child relationships are associated with children's internalizing problems. We examined longitudinal associations between mother-child conflict and children's internalizing problems in middle childhood. Specifically, we examined whether conflict and children's internalizing problems predict each other longitudinally in a sample of children from 3rd through 6th grade (N = 1,364) and their mothers using a cross-lagged panel model with random intercepts. In line with expectations, we found stable between-family differences in both mother-child conflict and children's internalizing problems. Contrary to expectations, we did not find that mother-child conflict and children's internalizing problems showed significant cross-lagged associations. However, mother-child conflict and children's internalizing problems had correlated errors at each wave, indicating that these two constructs covary with each other concurrently at multiple times across development, independent of stable between-family associations (i.e., as one increases, so does the other, and vice versa). The results of this study point to the importance of using statistical approaches that can disentangle between-family differences from within-family processes. In future studies, shorter time scales (e.g., weeks or months) may better capture dynamic associations between parent-child conflict and internalizing problems.
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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 | 0.004 |
| 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 | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".