ORIGINAL RESEARCH ARTICLE Performance improvement CME in psychiatry: implementing black
Bibliographic record
Abstract
Objective. Performance improvement continuing medical education (PI CME) is a recent educational methodology designed better to link educational content and outcomes in the context of limitations in the effectiveness of traditional CME. This study examines the ease of use and effectiveness of psychiatric CME in a small hospital. Methods. All staff psychopharmacologists assessed their performance in providing informed consent of black box risks of prescribed psychiatric medica-tions in a 3-month period. Staff were educated regarding black box risks of all commonly prescribed psychiatric medications, and their performance in the following quarter was reassessed. Significance of change following the educa-tional intervention at 3 and 6 months was determined by Chi square analysis. Results. PI CME was clearly successful in supporting behaviour change, χ2(1, N=60) =20.86, p=0.000, far outstripping traditional CME efforts. Changes in behaviour persisted over time, χ2(1, N=61)=4.04, p=0.044. This PI CME event received the highest possible rating by CME participants, and took few staff resources to implement. Conclusions. PI CME is an educational technology that can be carried out, without significant burdens to participants or educators, in psychiatric departments of small hospitals. PI CME can be much more effective than traditional CME in bringing about desired behaviour change in psychiatrist behaviours. Hospital-based PI CME may have other benefits, such as meeting criteria for Maintenance of Certification.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".