Establishing a National Quality Improvement Program for Nephrology Fellows: The Canadian Society of Nephrology Experience
Bibliographic record
Abstract
Purpose of program: In 2021, the Canadian Society of Nephrology (CSN) sent a needs assessment survey to nephrology residents, fellows, and program directors that identified a significant gap in Quality Improvement (QI) training. In response, the CSN's Quality Improvement and Implementation Science (CSN-QUIS) committee launched a national nephrology fellow QI curriculum in 2022. Methods: The program integrates online learning with interactive virtual didactic sessions, including participation in a longitudinal QI project that is presented at the CSN Annual General Meeting (CSN AGM). Key findings: Since inception, the program has expanded to 13 nephrology training programs, including both adult and pediatric sites. Forty-one fellows have completed the full curriculum, with 76 trainees having completed at least one year and presented work at the CSN AGM. Feedback from participants has been overwhelmingly positive, particularly regarding the interactive format, real-world applicability, and national networking opportunities. Continuous fine-tuning of the curriculum itself has occurred in parallel with refinements made to session content, project scheduling, and presentation format based on learner input. Limitations: Barriers such as limited local QI mentorship have been mitigated through virtual faculty pairing, and resources such as software access and publication support have been provided to encourage project success. Implications: Herein, we report the development and initial experience of this national initiative, demonstrating that a structured, collaborative, and distributed QI curriculum is feasible, effective, and scalable across Canada.
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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 | 0.028 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".