The Prevention of Child Physical Abuse and Neglect: An Update
Bibliographic record
Abstract
We reviewed research that has evaluated prevention programs for child abuse and neglect. A few universal educational and parenting programs (e.g., abusive head trauma educational programs, enhanced pediatric care interventions) have been found to be effective. Moreover, a few selective home visitation programs (e.g., the Nurse-Family Partnership program), have shown evidence that they can prevent child abuse and neglect. As well, there is some evidence that multi-component programs are successful. Finally, the research on the importance of program length and intensity as a moderator of program effectiveness is mixed. While the evidence base of effective prevention programs for child abuse and neglect is growing, current interventions are more likely to be program-focused than policy-focused, selective than universal, ameliorative than transformative, and directed at the micro-level than the macro-level. Unless prevention programs are accompanied by social policies that have an agenda of social justice, poverty reduction, and community capacity-building, their potential to prevent child abuse will be seriously challenged.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".