Quality education: A pilot quality improvement curriculum for psychiatry residents
Bibliographic record
Abstract
BACKGROUND: A series of Institute of Medicine's reports have highlighted the need for greater quality improvement (QI) training in medical education; however, few formal QI curricula for medical trainees have been described in the literature. AIM: The objective of this study was to develop a contextual QI curriculum involving a QI workshop and longitudinal QI projects (QIPs) for psychiatry trainees. METHODS: We examined psychiatry residents' attitudes on QI training following their exposure to a physician-manager curriculum using focus group methodology. Focus group data were used to inform revisions to the QI curriculum. Following the curriculum revisions, we administered a resident questionnaire to elicit resident perceptions on the modified QI curriculum. RESULTS: Focus group data from 40 psychiatry residents at the University of Toronto identified the following themes: challenges with QIP workload, difficulties of QI workshop integration into the curriculum, and value of the experiential component of the QIP. Of the 26 residents, 18 completed the resident questionnaire on the revised curriculum and reported an enhanced appreciation of QI in their current clinical practice. CONCLUSION: The study results suggest that this experiential format warrants further exploration as a model for QI training in medicine.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".