Editorial: Reflecting on child effects in psychology and psychiatry research
Bibliographic record
Abstract
The authors explore publication trends in the Journal of Child Psychology and Psychiatry over the past 5 years regarding associations between parenting and child outcomes, with a focus on the directionality of these effects. Bibliometric analysis revealed that far more studies have examined parent-to-child associations than either child-to-parent or bidirectional associations, reflecting a significant imbalance in what researchers publishing in JCPP intend to study. However, when evaluating evidence from a subset of robust and well-designed studies, especially those that permit a test of bidirectionality, the authors see a more balanced picture, with a roughly equal number of studies finding evidence of parent-to-child, child-to-parent, and bidirectional effects. These studies used a range of methodologies and examined a diverse set of parenting behaviors and child outcomes. Overall, the findings suggest that evidence in favor of child effects is consistently observed despite being significantly understudied relative to that of parent effects. The authors emphasize the importance of studying both child and parent effects alongside one another to understand the complexity of parent-child interactions, and underscore how respect for the agency and perspectives of youth is essential to understanding how they shape the conditions in which they grow up.
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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 | 0.013 | 0.060 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.022 | 0.028 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".