Child abuse-related homicides precipitated by caregiver use of harsh physical punishment
Bibliographic record
Abstract
BACKGROUND: Physical punishment (PP), which may involve the use of physical force, has been linked to negative effects in children and can escalate to abusive or harsh PP, resulting in injury or death. OBJECTIVE: To examine characteristics associated with fatal abuse involving caregiver use of harsh PP. METHODS: Data were from the National Violent Death Reporting System in 40 states, the District of Columbia, and Puerto Rico for years 2012-2018. Qualitative analysis was used to code textual material into categorial data, and logistic regression was used to examine associations between various characteristics and harsh PP. RESULTS: Approximately 4 % (n = 87) of the 2414 abuse-related homicides were known to have been precipitated by caregiver use of harsh PP. In adjusted models, homicides had greater odds of being harsh PP-related when incidents involved mothers' male companions (versus fathers), victims had a previous nonfatal injury (versus no previous nonfatal injury), and another adult participated in the fatal incident or had awareness of prior abuse/neglect (versus those without this characteristic). Two common precipitators of caregivers' use of harsh PP were: 1) child had a bathroom-related accident/soiled clothes (23.0 %; n = 20), and 2) child disobeyed a directive given by the perpetrator (17.2 %; n = 15). CONCLUSIONS: This study highlights characteristics associated with fatal abuse precipitated by caregiver use of harsh PP. Children were physically punished for developmentally normative behaviors. Ensuring caregivers are aware of and use effective parenting practices that focus on use of nonphysical discipline and promote healthy child development, may help decrease harsh PP and physical abuse-related homicides among children.
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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 | 0.006 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".