Physical Punishment and Mental Disorders: Results From a Nationally Representative US Sample
Bibliographic record
Abstract
BACKGROUND: The use of physical punishment is controversial. Few studies have examined the relationship between physical punishment and a wide range of mental disorders in a nationally representative sample. The current research investigated the possible link between harsh physical punishment (ie, pushing, grabbing, shoving, slapping, hitting) in the absence of more severe child maltreatment (ie, physical abuse, sexual abuse, emotional abuse, physical neglect, emotional neglect, exposure to intimate partner violence) and Axis I and II mental disorders. METHODS: Data were from the National Epidemiologic Survey on Alcohol and Related Conditions collected between 2004 and 2005 (N = 34653). The survey was conducted with a representative US adult population sample (aged ≥ 20 years). Statistical methods included logistic regression models and population-attributable fractions. RESULTS: Harsh physical punishment was associated with increased odds of mood disorders, anxiety disorders, alcohol and drug abuse/dependence, and several personality disorders after adjusting for sociodemographic variables and family history of dysfunction (adjusted odds ratio: 1.36-2.46). Approximately 2% to 5% of Axis I disorders and 4% to 7% of Axis II disorders were attributable to harsh physical punishment. CONCLUSIONS: Harsh physical punishment in the absence of child maltreatment is associated with mood disorders, anxiety disorders, substance abuse/dependence, and personality disorders in a general population sample. These findings inform the ongoing debate around the use of physical punishment and provide evidence that harsh physical punishment independent of child maltreatment is related to mental disorders.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".