Methodological standards for randomised controlled trials of interventions for preventing recurrence of child physical abuse and neglect
Bibliographic record
Abstract
Abstract Despite the significant financial and human resources invested in child protection services (CPS), it is unknown whether these services are effective in preventing recurrence of child physical abuse and neglect. This paper reviews available studies evaluating the effectiveness of these interventions and identifies methodological limitations and factors that may contribute to these limitations. We searched databases to identify randomised controlled trials published in peer‐reviewed journals in the past five decades that evaluated interventions to reduce recurrence of physical abuse and neglect. We outlined ten methodological standards that are important for rigorous testing of psychosocial interventions and applied them in critically appraising identified studies. Thirteen randomised controlled trials were reviewed. This review identified methodological limitations (e.g. small sample size, lack of standardisations, contamination) that made it difficult to draw reliable conclusions as to the effectiveness of interventions. Field‐specific factors that contributed to methodological limitations (e.g. heterogeneity of sample, multiple family problems, psychosocial nature of interventions) were identified and recommendations were provided for improvement. It was concluded that it is possible to implement high‐quality trials that are ethical and feasible in the child welfare field. Copyright © 2009 John Wiley & Sons, Ltd.
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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 | 0.709 | 0.859 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.024 |
| Bibliometrics | 0.020 | 0.019 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.011 | 0.007 |
| Research integrity | 0.017 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".