Attitudes and Opinions of Canadian Nephrologists Toward Continuous Quality Improvement Options
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: A shift to holding individual physicians accountable for patient outcomes, rather than facilities, is intuitively attractive to policy makers and to the public. We were interested in nephrologists' attitudes to, and awareness of, quality metrics and how nephrologists would view a potential switch from the current model of facility-based quality measurement and reporting to publically available reports at the individual physician level. DESIGN SETTING PARTICIPANTS AND MEASUREMENTS: The study was conducted using a web-based survey instrument (Online Appendix 1). The survey was initially pilot tested on a group of 8 nephrologists from across Canada. The survey was then finalized and e-mailed to 330 nephrologists through the Canadian Society of Nephrology (CSN) e-mail distribution list. The 127 respondents were 80% university based, and 33% were medical/dialysis directors. RESULTS: The response rate was 43%. Results demonstrate that 89% of Canadian nephrologists are engaged in efforts to improve the quality of patient care. A minority of those surveyed (29%) had training in quality improvement. They feel accountable for this and would welcome the inclusion of patient-centered metrics of care quality. Support for public reporting as an effective strategy on an individual nephrologist level was 30%. CONCLUSIONS: Support for public reporting of individual nephrologist performance was low. The care of nephrology patients will be best served by the continued development of a critical mass of physicians trained in patient safety and quality improvement, by focusing on patient-centered metrics of care delivery, and by validating that all proposed new methods are shown to improve patient care and outcomes.
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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 | 0.013 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".