Multiple maltreatment, attribution of blame, and adjustment among adolescents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it