Multiple maltreatment, attribution of blame, and adjustment among adolescents
Bibliographic record
Abstract
The study examined the predictive utility of blame attributions for maltreatment. Integrating theory and research on blame attribution, it was predicted that self-blame would mediate or moderate internalizing problems, whereas other-blame would mediate or moderate externalizing problems. Mediator and moderator models were tested separately. Adolescents (N = 160, ages 11-17 years) were randomly selected from the open caseload of a child protection agency. Participants made global maltreatment severity ratings for each of physical abuse, psychological abuse, neglect. sexual abuse, and exposure to family violence. Participants also completed the Attribution for Maltreatment Interview (AFMI), a structured clinical interview that assessed self- and perpetrator blame for each type of maltreatment they experienced. The AFMI yielded five subscales: self-blaming cognition, self-blaming affect, self-excusing. perpetrator blame, and perpetrator excusing. Caretaker-reported (Child Behavior Checklist) and self-reported (Youth Self Report) internalizing and externalizing were the adjustment criteria. Controlling for maltreatment severity, the AFMI subscales explained significant variance in self-reported adjustment. Self-blaming affect was the most potent attribution, particularly among females. Attributions mediated maltreatment severity for self-reported adjustment but moderated it for caretaker-reported adjustment. The sophistication and relevance of blame attributions to adjustment are discussed, and implications for research and clinical practice are identified.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".