Childhood maltreatment, posttraumatic stress symptomatology, and adolescent datingviolence: Considering the value of adolescent perceptions of abuse and a trauma mediationalmodel
Bibliographic record
Abstract
The present study, utilizing both a child protective services and high school sample of midadolescents, examined the issue of self-report of maltreatment as it relates to issues of external validity (i.e., concordance with social worker ratings). reliability (i.e.. overlap with an alternate child maltreatment self-report inventory; association of a self-labeling item as "abused" with their subscale item counterparts), and construct validity (i.e., the association of maltreatment with posttraumatic stress symptomatology and dating violence). Relevant theoretical work in attachment, trauma, and relationship violence points to a mediational model, whereby the relationship between childhood maltreatment and adolescent dating violence would be expected to be accounted for by posttraumatic stress symptomatology. In the high school sample, 1329 adolescents and, in the CPS sample, 224 youth on the active caseloads completed comparable questionnaires in the three domains of interest. For females only, results supported a mediational model in the prediction of dating violence in both samples. For males, child maltreatment and trauma symptomatology added unique contributions to predicting dating violence. with no consistent pattern emerging across samples. When considering the issue of self-labeling as abused. CPS females who self-labeled had higher posttraumatic stress symptomatology and dating violence victimization scores than did their nonlabeling, maltreated counterparts for emotional maltreatment. These results point to the need for ongoing work in understanding the process of disclosure and how maltreatment experiences are consciously conceptualized.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".