Predictors of parent training efficacy for child externalizing behavior problems – a meta‐analytic review
Bibliographic record
Abstract
BACKGROUND: The differential effectiveness of parent training has led researchers to examine a variety of child, parent, and familial variables that may predict treatment response. Studies have identified a diverse set of child, parent psychological/behavioral and demographic variables that are associated with treatment outcome and dropout. METHOD: The parent training literature was examined to isolate child, parent, and family variables that predict response to parent training for child externalizing behavior problems. A literature review was conducted spanning articles published from 1980 to 2004 of indicated prevention (children with symptoms) and treatment (children with diagnosis) studies. Meta-analyses were conducted to determine standardized effect sizes associated with the identified predictors. RESULTS: Many of the predictors of treatment response examined in this meta-analysis resulted in moderate standardized effect sizes when study results were subjected to meta-analytic procedures (i.e., low education/occupation, more severe child behavior problems pretreatment, maternal psychopathology). Only low family income resulted in a large standardized effect size. Predictors of drop-out resulted in standardized effect sizes in the small or insubstantial range. CONCLUSIONS: Response to parent training is often influenced by variables not directly involving the child, with socioeconomic status and maternal mental health being particularly salient factors.
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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 | 0.010 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.012 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".