Evaluating a Modular Approach to Therapy for Children With Anxiety, Depression, Trauma, or Conduct Problems (MATCH) in School-Based Mental Health Care: Study Protocol for a Randomized Controlled Trial
Bibliographic record
Abstract
Introduction: Schools have become a primary setting for providing mental health care to youths in the U.S. School-based interventions have proliferated, but their effects on mental health and academic outcomes remain understudied. In this study we will implement and evaluate the effects of a flexible multidiagnostic treatment called Modular Approach to Therapy for Children with Anxiety, Depression, Trauma, or Conduct Problems (MATCH) on students' mental health and academic outcomes. Methods and Analysis: This is an assessor-blind randomized controlled effectiveness trial conducted across five school districts. School clinicians are randomized to either MATCH or usual care (UC) treatment conditions. The target sample includes 168 youths (ages 7–14) referred for mental health services and presenting with elevated symptoms of anxiety, depression, trauma, and/or conduct problems. Clinicians randomly assigned to MATCH or UC treat the youths who are assigned to them through normal school referral procedures. The project will evaluate the effectiveness of MATCH compared to UC on youths' mental health and school related outcomes and assess whether changes in school outcomes are mediated by changes in youth mental health. Ethics and Dissemination: This study was approved by the Harvard University Institutional Review Board (IRB14-3365). We plan to publish the findings in peer-reviewed journals and present them at academic conferences. Clinical Trial Registration: ClinicalTrials.gov ID: NCT02877875. Registered on August 24, 2016.
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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 | 0.044 | 0.042 |
| Meta-epidemiology (narrow) | 0.008 | 0.004 |
| Meta-epidemiology (broad) | 0.016 | 0.008 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.067 | 0.011 |
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".