Measurement-based Care Training Curriculum in Psychiatry Residency Programs: Four-year Implementation Experience and Future Directions.
Bibliographic record
Abstract
OBJECTIVE: In 2019, the authors began implementing a measurement-based care (MBC) curriculum into two residency programs at West Virginia University (WVU) and Delaware Psychiatric Center (DPC). The authors present findings from the four-year implementation period and describe a web-based MBC course that aims to train attendings and residents across the United States (US) and abroad. METHODS: The web-based MBC course includes four readings (the MBC instruction manual, the Standard for Clinicians' Interview in Psychiatry [SCIP] glossary, clinician-administered [CA] scales, and self-administered [SA] scales), four didactic presentations (MBC basics, psychopathology assessment, epidemiological concepts, and psychiatric measures), and four video interviews. The web-based MBC course is accessible through the WVU online continuing medical education (CME) web courses. The modified MBC psychiatry residency training curriculum includes four didactic lectures taught by MBC-trained faculty members and attendings. Residents practice using the scales during their inpatient and outpatient rotations and complete the web-based MBC course before graduation. RESULTS: The web-based MBC course was used to train most of the attendings in the WVU and DPC residency programs. Both programs now require residents to complete the web-based MBC course before graduation. Of the 52 residents in both programs, 26 residents (50%) had completed the training at the time of writing this article. CONCLUSION: The web-based MBC course was successfully implemented in two US residency programs and is now available for clinicians around the world to access. Free access to the SCIP scales will be granted to psychiatry residency programs implementing the MBC curriculum.
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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 | 0.011 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".