Preventing child maltreatment: An evidence-based update
Bibliographic record
Abstract
Child maltreatment is a significant public health problem associated with a broad range of negative outcomes in children and adolescents that can extend into adulthood. This review summarizes information about programs aimed at the prevention of child maltreatment evaluated by controlled trials, with a focus on home visitation programs. It does not include programs aimed at prevention of child sexual abuse, the subject of a separate review in this series. We discuss those programs that include one or more measures of child maltreatment and related outcomes (reports of abuse and neglect, injuries, hospitalizations and emergency room visits). Most programs targeting at-risk families have not shown evidence of effectiveness in preventing abuse or neglect. An important exception is the Nurse Family Partnership (NFP), a program provided by nurses to first-time socially disadvantaged mothers beginning prenatally that has undergone rigorous evaluation in three randomized controlled trials. It has shown consistent effects in reducing reports of maltreatment and associated outcomes as well as additional benefits in maternal and child health in high-risk families. A second exception is the promising Early Start program provided by nurses and social workers to at-risk families beginning postnatally. One randomized controlled trial of the program has shown reduced rates of parental reports of severe abuse and hospital attendance for injuries and poisonings, based on records. The characteristics of the NFP and Early Start programs are discussed with special emphasis on ways in which they differ from other home visitation programs.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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; 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".