Evidence-based training in treatment treatments: The case of pediatric psychopharmacology
Bibliographic record
Abstract
Introduction Despite major research advances in pediatric psychopharmacology and psychotherapy over the last 20 years, the lack of well-trained specialists has posed almost insurmountable barriers to many children and families from receiving high-quality, evidence-based assessment and treatments. The REACH Institute, an international non-profit organization dedicated to disseminating evidence-based treatments, has developed portable, effective methods to train primary care and specialty health providers in pediatric psychopharmacology and psychotherapies. Objectives To develop effective, scientifically-proven methods for teaching and disseminating evidence-based treatments. Aims To develop, disseminate, and evaluate high quality approaches for teaching primary care and specialty mental health practitioners in pediatric psychopharmacology. Methods Using novel approaches grounded in scientific behavioral change technologies, over 700 health care providers have been trained in evidence-based pediatric psychopharmacology in sites across multiple countries, including the US, Canada, and Norway. All trainings are rigorously evaluated for changes in health care practitioners' behaviors and clinical practices, including within an NIH-funded randomized controlled trial (RCT). Results Trainings have been very well-received across multiple countries, requiring only minor adaptations. These trainings have been effective in yielding changes in health care practitioners abilities and actual clinical practices, enabling more children to access appropriate pediatric psychopharmacology. Conclusions Effective and disseminable methods for changing health care practitioners behaviors in applying pediatric psychopharmacology treatments are possible, and can be adapted to different countries, languages, and cultural contexts.
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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 | 0.167 | 0.287 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".