An Interdisciplinary University-Based Initiative for Graduate Training in Evidence-Based Treatments for Children’s Mental Health
Bibliographic record
Abstract
States and jurisdictions are under increased pressure to demonstrate the use of evidence-based treatments (EBTs) for children’s mental health, increasing the demand for a workforce trained in these practices. Universities are a critical pipeline for this workforce. This article describes the genesis and evolution of a university-based initiative for training in EBTs for children, youth, and families. Given both the need to make training in EBTs available to future providers in a range of disciplines and that mental health providers increasingly find themselves on interdisciplinary teams (despite university-based training being relatively siloed along disciplinary lines), the initiative has had an interdisciplinary focus. Two tracks are described: (a) Practitioner Track, a course series in which students learn a specific EBT, and (b) Referral Track, a monthly lecture series designed to engage a wider university and community audience. Results of the program evaluation component of this initiative revealed that students can significantly increase their skills and self-efficacy in components of EBT delivery through participation in the active, skill-focused courses. Furthermore, the results of the lecture series evaluation appear to meet an important need for community-based providers and other supportive individuals in transferring useful knowledge about best practices. Implications and future directions are discussed.
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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 | 0.020 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".