Nature and severity of physical harm caused by child abuse and neglect: results from the Canadian Incidence Study.
Bibliographic record
Abstract
BACKGROUND: Despite growing public concern about child maltreatment, the scope and severity of this significant public health issue remains poorly understood. This article examines the nature and severity of the physical harm associated with reports of child maltreatment documented in the Canadian Incidence Study of Reported Child Abuse and Neglect (CIS). METHODS: The CIS collected information directly from child welfare investigators about cases of reported child abuse or neglect. A multistage sampling design was used to track child-maltreatment investigations conducted at selected sites from October to December 1998. The analyses were based on the sample of 3780 cases in which child maltreatment was substantiated. RESULTS: Some type of physical harm was documented in 18% of substantiated cases; most of these involved bruises, cuts and scrapes. In 4% of substantiated cases, harm was severe enough to require medical attention, and in less than 1% of substantiated cases, medical attention was sought for broken bones or head trauma. Harm was noted most often in cases of physical abuse compared to other forms of maltreatment. INTERPRETATION: Rates of physical harm were lower than expected. Current emphasis on mandatory reporting, abuse investigations and risk assessment may need to be tempered for cases in which physical harm is not the central concern.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".