Practical Tips for Teaching Postgraduate Residents Continuous Quality Improvement
Bibliographic record
Abstract
Background: Continuous quality improvement (CQI) in health care involves changes that result in better clinical and process outcomes.Accreditation bodies are mandating postgraduate educational programs to teach CQI among residents, although their baseline knowledge and experience vary tremendously.There is no single effective method to teach CQI.Aim: To develop a comprehensive CQI curriculum for residents.Work Done: This article describes the experiences of developing a CQI curriculum for trainees working in the university internal medicine residency program in Vancouver, British Columbia, Canada.We report the key elements for other educational programs interested in developing similar curricula.Conclusions: A formal CQI curriculum that teaches basic theory and includes an independent, focused project is a useful model, and broad dissemination is advisable.There should be protected time for teaching and learning, using interactive and case-based methodology.Communication and collaboration skills can be emphasized.Longitudinal and face to face mentoring are helpful.An open forum on CQI can raise awareness, and a separate assessment and reward system can motivate residents.Further training opportunities for faculty and interested residents should be available.Hospital staff and departmental support is essential.The CQI curriculum needs to undergo continuous improvement itself.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.057 | 0.021 |
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".