Effectiveness and Predictors of Outcomes in a Psychiatric Day Treatment Program for Elementary-Age
Bibliographic record
Abstract
OBJECTIVE: In this study, we aimed to (1) assess the effectiveness of an intensive multimodal day treatment program in improving externalizing problems and function in elementary-age children and (2) examine 3 predictors of the treatment outcome (i.e., family functioning, baseline severity, and comorbid disorders). METHODS: The sample included 261 children (80.9% boys) between ages of 5 and 12. A retrospective chart review, from 2013 to 2018, and a prospective chart review, from 2018 to 2019, were conducted to extract all relevant data for the present study. Parents and teachers provided reports on children's externalizing problems (i.e., aggressive behavior, attention problems, and rule-breaking behavior) and their level of function across different domains. The level of family functioning was also reported by parents, while clinicians assessed children's severity of disturbance and their diagnoses at intake. RESULTS: Based on both parents' and teachers' reports, children showed significant improvement in their externalizing problems. Moreover, children showed functional improvement at home, at school, with peers, and in hobbies by the end of the program. Based on teacher's reports, children with lower level of severity showed less improvement in their attention problems, and those with comorbid developmental problems showed less improvement in their aggressive and rule-breaking behaviors. Family functioning did not predict any treatment outcome. CONCLUSION: An intensive multimodal day treatment program was effective in reducing the symptoms of externalizing problems in elementary-age children. However, children with less severe difficulties and comorbid developmental problems showed less improvement in their externalizing problems.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".