Early Physical Abuse and Adult Outcomes
Bibliographic record
Abstract
BACKGROUND: Because most physical abuse goes unreported and researchers largely rely on retrospective reports of childhood abuse or prospective samples with substantiated maltreatment, long-term outcomes of physical abuse in US community samples are unknown. We hypothesized that early childhood physical abuse would prospectively predict adult outcomes in education and economic stability, physical health, mental health, substance use, and criminal behavior. METHODS: Researchers in two multisite studies recruited children at kindergarten entry and followed them into adulthood. Parents completed interviews about responses to the child's problem behaviors during the kindergarten interview. Interviewers rated the probability that the child was physically abused in the first 5 years of life. Adult outcomes were measured by using 23 indicators of education and economic stability, physical health, mental health, substance use, and criminal convictions reported by participants and their peers and in school and court records. RESULTS: Controlling for potential confounds, relative to participants who were not physically abused, adults who had been abused were more likely to have received special education services, repeated a grade, be receiving government assistance, score in the clinical range on externalizing or internalizing disorders, and have been convicted of a crime in the past year (3.20, 2.14, 2.00, 2.42, 2.10, and 2.61 times more likely, respectively) and reported levels of physical health that were 0.10 SDs lower. No differences were found in substance use. CONCLUSIONS: Unreported physical abuse in community samples has long-term detrimental effects into adulthood. Pediatricians should talk with parents about using only nonviolent discipline and support early interventions to prevent child abuse.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".