Profiles of self-regulation and their association with behavior problems among sexually abused children
Bibliographic record
Abstract
Abstract This study aimed to delineate profiles of self-regulation among sexually abused children and their association with behavior problems using a person-centered approach. A sample of 223 children aged six to 12, their parents, and teachers were recruited in specialized intervention centers. Latent profile analysis revealed four profiles: (1) Dysregulated, (2) Inhibited, (3) Flexibly Regulated, and (4) Parent Perceived Self-Regulation. Children from the Flexibly Regulated profile showed relatively low behavior problems, and those from the Dysregulated profile were characterized by high behavior problems. Children from the Parent Perceived Self-Regulation profile showed overall good adaptation, although teachers reported higher behavior problems than parents. Children from the Inhibited profile, characterized by the highest level of inhibition but low parent-rated emotion regulation competencies and executive functions, showed the highest level of internalizing behavior problems, indicating that high inhibition does not necessarily translate to better adaptation. Results also show a moderation effect of sex. Being assigned to the Inhibited profile was associated with decreased externalizing behaviors in boys and increased internalizing behaviors in girls. This study underscores the complexity of self-regulation in sexually abused children and supports the need to adopt a multi-method and multi-informant approach when assessing these children.
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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 | 0.000 | 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) | 0.001 | 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".