SAFE QI – a framework to overcome the challenges of implementing a quality improvement curriculum into a residency program
Bibliographic record
Abstract
Quality improvement (QI) is an essential component of medical practice. Medical students and residents must learn the skills to conduct clinical QI during their educational programs. Medical educators must create and implement a curriculum in QI to empower their students to develop this skill and knowledge. However, developing and implementing a QI curriculum may be challenging for some residency programs. Residency programs with a relatively short duration of training - for example, only 2 years - may be unable to implement an extensive QI curriculum without siphoning away time for other learning objectives. Small residency programs may lack faculty with expertise to teach this topic. Residency programs with only a few residents may find it difficult to evaluate the success of a QI curriculum using robust statistical analysis. These residency programs need a QI curriculum with several features. The curriculum must be deliverable in a short period of time. There must be tools to assess the residents' attainment of the curricular objectives. The curriculum must give the residents practical skills to develop their own QI initiatives. Finally, there must be simple methods to evaluate the curriculum's effectiveness. To address these goals, we developed the SAFE QI (QI curriculum which is short, assessed, functional, and effective) framework for the 2-year subspecialty respirology residency program at the University of Alberta. There are 2-3 entrants per year for a total of 4-6 residents. This framework helps medical educators overcome the challenges of implementing a QI curriculum into their educational programs. This article illustrates how this framework was used to develop and deliver an institution's own QI 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 | 0.025 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".