From Theory to Practice: Development and Evaluation of a Quality Improvement Curriculum for Psychiatry Residents
Bibliographic record
Abstract
OBJECTIVES: Quality improvement (QI) is a systematic approach used to analyze and address problems in healthcare. Evidence of its success has led some national regulatory bodies to require QI education in residency training programs. However, limited work to date has demonstrated how residency programs can integrate best practices in QI education to design their own curriculum. This study describes the implementation and evaluation of a new QI curriculum, grounded in a theoretical model of how QI education works, for Canadian psychiatry residents. METHODS: PGY-2 and PGY-4 psychiatry residents received a 2.5-h mixed didactic and simulation-based QI workshop as a part of the 2021-2022 academic curriculum. Their knowledge and attitudes toward QI were assessed using the QI Knowledge Application Tool Revised (QIKAT-R) and the Beliefs and Attitudes subscale of the Beliefs, Attitudes, Skills, and Confidence in QI (BASiC-QI). RESULTS: Eleven of 12 residents (92%) who completed the curriculum participated in the study. Average QIKAT-R scores improved from 4.45 to 7.00. Average BASiC-QI Beliefs and Attitudes subscale scores increased by 5.55 points. Residents reported enjoying QI and an increased desire to participate in future QI projects. CONCLUSION: This study demonstrates how a programme theory of QI education can be used to develop an effective, locally-tailored curriculum. This approach can be replicated by other educators to develop or improve QI curricula.
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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 | 0.032 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".