Nonaccidental Injuries in Adopted Children: A Systematic Review
Bibliographic record
Abstract
Nonaccidental injuries (NAI) and child abuse in adopted children are pressing public health issues, shaped by pre-adoption adversities and post-adoption stressors. This systematic review synthesizes evidence on NAI prevalence, risk factors, screening tools, and perpetrator patterns in adopted children under 16. We searched PubMed, Google Scholar, and EMBASE on October 15, 2024, using terms like “nonaccidental injuries,” “child abuse,” and “adoption,” yielding 2,847 studies from 1990–2025. Twelve studies (case series, case reports, prospective/retrospective) met inclusion criteria. Neglect (70%) and physical abuse (45%) were predominant, with fractures (70%, 40% transverse, 35% skull) and bruises (45%) most common, linked to institutional neglect, socioeconomic challenges, and transracial adoption. Parents/caregivers were frequent perpetrators, aligning with broader maltreatment trends. The International Society for the Prevention of Child Abuse and Neglect (ISPCAN) Child Abuse Screening Tool (ICAST), a 10-item questionnaire for detecting physical, sexual, emotional abuse, and neglect, was used in 5/12 studies, highlighting a gap in standardized screening. Study quality, assessed via the Newcastle-Ottawa Scale, was moderate (7/9) due to inconsistent follow-up and comparability. This review underscores adopted children’s vulnerability due to adverse childhood experiences, urging adoption-specific screening tools, trauma-informed interventions, and enhanced post-adoption support. Longitudinal research and policy reforms are essential to address cultural/socioeconomic risks and reduce maltreatment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".