Diversity of adaptation profiles in youth victims of child sexual abuse.
Bibliographic record
Abstract
OBJECTIVE: Child sexual abuse (CSA) is associated with long-term negative consequences in adolescents, but some survivors display resilience. The purpose of this study was to delineate profiles of adaptation in adolescent victims of CSA and to examine their associations with individual and environmental-systemic protective factors. METHOD: As part of a population-based survey, 8,230 high school students were questioned about CSA and completed measures assessing a host of protective factors and indicators of positive adaptation across 5 domains: self-perception, academic success, mental health, health risk behaviors and romantic relationships. RESULTS: Using a latent class analysis, a best fitting model of 4 classes was identified. This model included a reference group of nonsexually abused teenagers and 3 classes characterizing survivors of CSA: Resilient profile (33% of youth), Externalized profile (34% of youth) and Internalized profile (33% of youth). Sexually abused youth assigned to the Resilient profile were similar to nonsexually abused youth in terms of self-esteem, academic performance, absence of clinical levels of psychological distress and dating violence. Despite experiencing CSA of comparable severity, youth in the Resilient profile reported more optimism and were less likely to rely on avoidant or emotional strategies to cope with difficulties and more likely to report high maternal and paternal support. CONCLUSIONS: Findings highlight the utility of a person-oriented approach to enhance our understanding of the diversity of adaptation profiles in youth victims of CSA. Results also underscore the importance of tailoring intervention efforts to efficiently tackle the diverse needs of teen victims of CSA. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.003 |
| 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 | 0.000 |
| 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".