Immediate and short‐term outcomes of the ‘COPEing with Toddler Behaviour’ parent group
Bibliographic record
Abstract
BACKGROUND: Controlling, uninvolved, and rejecting parenting in early childhood are strong predictors of later disruptive behavior disorders. However, there have been no evaluations of non-targeted groups for parents of very young children, despite their potential advantages. METHODS: We randomly assigned 79 mothers of 12- to 36-month-olds to an 8-session parent training program (called 'COPEing with Toddler Behaviour') or to a waiting list control condition. We investigated the immediate and short-term impact on parent-reported child behavior problems, observed parent-child interaction, and self-reported parenting behavior and parent functioning. RESULTS: In an intent-to-treat design, the program yielded significant effects on child behavior problems, positive parent-child interaction, and parental overreactivity and depression but not observed negative child behavior or parental laxness. Most effects were significant at both post-test and 1-month follow-up and effects sizes were small to medium for the intervention group and inverse to small for the control group. CONCLUSIONS: The potential of the program to prevent later behavior problems is supported by improvements in six of the eight outcomes. As part of a community strategy, groups such as COPEing with Toddler Behaviour may promote positive parent-child interaction and children's mental health.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".