A Systematic Review of the Outcome of Child Abuse in Long-Term Care
Bibliographic record
Abstract
The aim of the systematic review described in this article was to determine the outcome of child maltreatment in long-term childcare and the scope of the evidence base in this area. Searches of 10 databases were conducted. Forty-nine documents describing 21 primary studies and 25 secondary studies were selected for review. Searches, study selection, data extraction, and study quality assessments were independently conducted by two researchers, with a high degree of interrater reliability. Participants in the 21 primary studies included 3,856 abuse survivors and 1,577 nonabused controls. In six primary studies, survivors were under 18 years, and participants in the remaining primary studies were adults with a mean age of 54 years. Reviewed studies were conducted in the United Kingdom, the United States, Finland, Romania, Tanzania, Canada, Ireland, Australia, the Netherlands, Germany, Austria, and Switzerland. Participants were abused in religious and nonreligious residential care centers and foster care. There were significant associations between the experience of child abuse in long-term care and adjustment across the life span in the domains of mental health, physical health, and psychosocial adjustment. Evidence-based trauma-focused treatment should be offered to child abuse survivors. Future research in this area should prioritize longitudinal studies.
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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 | 0.009 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".