EVALUATING SCHOOL-BASED INTERVENTIONS AND PROGRAMS IN TREATING CHILDREN MENTAL HEALTH DISORDERS
Bibliographic record
Abstract
Background: The implementation of mental health services such as school-based interventions and programs have been well established within schools. However, it is uncertain how effective school-based interventions and programs are in treating children who are diagnosed with a mental health disorder specifically for children who are younger than 11 years old as it has not been explored. Objective: This proposed quasi-experimental study aims to determine the effectiveness of school-based mental health intervention programs in supporting and treating children (ages 6 to 11) who are diagnosed with a mental health disorder by evaluating their symptoms. Method: The proposed study would collect quantitative data from 200 participants recruited using a probability sampling strategy known as cluster sampling coupled with purposive sampling from Ontario-Montclair School District and Pomona Unified School District. Out of the 200 participants, 100 of the participants will be from OMSD and receive the school-based intervention and the other 100 participants will be from PUSD and not receive the school-based intervention. This study proposes the use of the Pediatric Symptom Checklist (PSC-35) pretreatment and posttreatment to measure changes in their symptoms, behaviors, interpersonal functioning, and emotions.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".