Physical Child Abuse Potential in Adolescent Girls: Associations with Psychopathology, Maltreatment, and Attitudes toward Child-Bearing
Bibliographic record
Abstract
OBJECTIVE: Adolescent mothers are at increased risk of mistreating their children. Intervening before they become pregnant would be an ideal primary prevention strategy. Our goal was to determine whether psychopathology, exposure to maltreatment, preparedness for child-bearing, substance use disorders (SUDs), IQ, race, and socioeconomic status were associated with the potential for child abuse in nonpregnant adolescent girls. METHOD: The Child Abuse Potential Inventory (CAPI) was administered to 195 nonpregnant girls (aged 15 to 16 years; 54% African American) recruited from the community. Psychiatric diagnoses from a structured interview were used to form 4 groups: conduct disorder (CD), internalizing disorders (INTs; that is, depressive disorder, anxiety disorder, or both), CD + INTs, or no disorder. Exposure to maltreatment was assessed with the Childhood Trauma Questionnaire, and the Childbearing Attitudes Questionnaire measured maternal readiness. RESULTS: CAPI scores were positively correlated with all types of psychopathology, previous exposure to maltreatment, and negative attitudes toward child-bearing. IQ, SUDs, and demographic factors were not associated. Factors associated with child abuse potential interacted in complex ways, but the abuse potential of CD girls was high, regardless of other potentially protective factors. CONCLUSIONS: Our study demonstrates that adolescent girls who have CD or INT are at higher risk of perpetrating physical child abuse when they have children. However, the core features of CD may put this group at a particularly high risk, even in the context of possible protective factors. Treatment providers should consider pre-pregnant counselling about healthy mothering behaviours to girls with CD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".