Addressing the Complexity of Heterogeneity: Profiles of Adolescent Girls Who Have Been Sexually Abused
Bibliographic record
Abstract
Childhood sexual abuse (CSA) may have devastating effects, yet, there is considerable heterogeneity among adolescent girls who have experienced it. Addressing this heterogeneity could help to tailor practices to their particular needs. The objective was to identify profiles among adolescent girls who have been sexually abused to determine whether they exhibit distinct outcomes. Participants were drawn from a Child Protection sample of adolescent girls who have been sexually abused with contact (n = 185). Abuse and stressful events were measured using a rating scale completed by a research assistant, and a self-reported questionnaire. Coping strategies, cognitive appraisals, and psychological symptoms were measured using self-reported questionnaires. Latent class analysis was conducted using abuse and stressful events as indicators, and multinomial logistic regression analyses were used to compare classes on outcomes. Five graded classes were identified: 1) few source of stress (22%); 2) services as stressors (27%); 3) CSA as stressor (19%); 4) CSA and family as stressors (6%); and 5) multiple sources of stress (25%). These classes were associated with distinct profiles on coping strategies, cognitive appraisals, and psychological symptoms. In conclusion, we recommend that clinicians move beyond the "one size fits all" approach and tailor practices to each adolescent's needs.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".