Developmental Trajectory of Conduct Problems Among Boys and Girls Receiving Psychoeducational Services at Elementary Schools
Bibliographic record
Abstract
Elementary public schools remain the most common venues for addressing children's severe conduct problems. Nevertheless, very few longitudinal studies have examined association between receiving psychoeducational services for conduct problems in school and subsequent conduct problem severity. This study explored if psychoeducational service reception contributed to reduce conduct problems in a sample of 434 elementary school-aged boys and girls presenting a high level of conduct problems. The study used a repeated measures design at 12-month intervals, for 4 years. Information regarding the severity of children's conduct problems and services was provided by parents and teachers. Latent Growth Modeling was used to identify a mean trajectory of conduct problems. Results revealed that psychoeducational services were associated with a decrease in conduct problems over time, but this association was only observed in boys. There was no association between service reception at study inception and the trajectory of conduct problems among girls. These results suggests that psychoeducational services are well suited to the difficulties of boys with conduct problems; however, they may call for a review of the services offered to girls in schools, both in terms of the detection of conduct problems in young girls, and in terms of their treatment options.
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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 | 0.002 |
| 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 | 0.001 | 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".