The relationships between harsh physical punishment and child maltreatment in childhood and intimate partner violence in adulthood
Bibliographic record
Abstract
BACKGROUND: Physical punishment of children is an important public health concern. Yet, few studies have examined how physical punishment is related to other types of child maltreatment and violence across the lifespan. Therefore, the objective of the current study was to examine if harsh physical punishment (i.e., being pushed, grabbed, shoved, hit, and/or slapped without causing marks, bruises, or injury) is associated with an increased likelihood of more severe childhood maltreatment (i.e., physical abuse, emotional abuse, sexual abuse, physical neglect, emotional neglect, and exposure to intimate partner violence (IPV)) in childhood and perpetration or victimization of IPV in adulthood. METHODS: Data were drawn from the National Epidemiologic Survey on Alcohol and Related Conditions collected in 2004 to 2005 (n = 34,402, response rate = 86.7%), a representative United States adult sample. RESULTS: Harsh physical punishment was associated with increased odds of childhood maltreatment, including emotional abuse, sexual abuse, physical abuse, physical neglect, emotional neglect, and exposure to IPV after adjusting for sociodemographic factors, family history of dysfunction, and other child maltreatment types (range 1.6 to 26.6). Harsh physical punishment was also related to increased odds of experiencing IPV in adulthood (range 1.4 to 1.7). CONCLUSIONS: It is important for parents and professionals working with children to be aware that pushing, grabbing, shoving, hitting, or slapping children may increase the likelihood of emotional abuse, sexual abuse, physical abuse, physical neglect, emotional neglect, and exposure to IPV in childhood and also experiencing IPV victimization and/or perpetration in later adulthood.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".