Building Strong Clinicians: Education Strategies to Promote Interest and Readiness for Evidence-Based Practice
Bibliographic record
Abstract
The challenges of including evidence-based practice (EBP) and evidence-based treatments (EBTs) in social work education continue to be discussed in the literature. As the behavioral health system moves toward greater practitioner accountability and expanded implementation of EBTs, it becomes increasingly important to prepare students for this type of practice. A successful provider-developed curriculum, designed to prepare students for extensive EBT job opportunities in Connecticut, was disseminated to local graduate schools through a faculty fellowship. This article discusses provider and faculty perspectives about course design elements that contributed to its effectiveness, as well as how the course was subsequently adapted to online learning for Master of Social Work (MSW) students, with evidence of positive impact on clinician development.
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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 | 0.031 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".