A latent class analysis of parent–child discrepancies in reports of peer victimization: Associations to child sexual abuse status and psychological adjustment
Bibliographic record
Abstract
Researchers face an important challenge when assessing peer victimization in children, since self-reports are often discrepant with parent-reports. A latent class analysis identified patterns of response to items assessing peer victimization, which were either divergent or convergent between the parent and the child. Classes were then compared on the child sexual abuse status and on various behavioral and social outcomes. Participants were 720 school-aged child victims of sexual abuse and a comparison group of 173 nonvictims and their caregivers. We identified two discordant subgroups (self-identified and parent-identified) and two concordant groups (nonvictims and concordant victims of peer victimization). Compared to children of the comparison group, sexually abused children were five times more likely to be identified as targets of peer victimization solely by their parent than the contrary. Sexually abused children with concordant reports of peer victimization showed the poorest adjustment on all studied outcomes assessed 6 months later. Children who discounted experiencing peer victimization while their parent reported it were also at risk of maladjustment. Results underscore the importance of supplementing self-reports with other available sources of information, especially in young and vulnerable populations who may be inclined to discount their victimization experiences.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".