The reporting of maltreatment experiences during the Adult Attachment Interview in a sample of pregnant adolescents
Bibliographic record
Abstract
This present student examines maltreatment experiences reported by 55 high-risk pregnant adolescents in response to a slightly adapted version of the Adult Attachment Interview (AAI; George, Kaplan, & Main, 1996 ). Previous research has suggested that the rates of unresolved states of mind regarding trauma in response to the AAI may be underestimated due to the lack of direct questions and associated probes regarding physical, sexual, and emotional abuse. We address this concern by including behaviorally phrased questions and probes regarding maltreatment experiences into the original format of the AAI and examine the concordance between reports of maltreatment experiences in response to the AAI and the Childhood Trauma Questionnaire (CTQ). Maltreatment experiences in response to the AAI were evaluated using the Maltreatment Classification Scale developed by Barnett, Manly, and Cicchetti (1993). We also examine the association between unresolved states of mind and dissociation using the Adolescent Dissociative Experience Scale. Results revealed a significant concordance between reports of maltreatment in response to the AAI and CTQ measures. Reports of maltreatment were prevalent in this sample: across the AAI and CTQ measures, 96% of pregnant adolescents reported some form of emotional abuse, 84% physical abuse, 59% sexual abuse, and 88% reported neglect. Sexual abuse history uniquely predicted unresolved status in response to the AAI. Self-reports of dissociation were significantly associated with unresolved states of mind. Results suggest that the inclusion of behaviorally focused questions and probes regarding maltreatment in the AAI protocol can further contribute to the clinical and theoretical value of this tool.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 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".