Strategies for Developing and Recognizing Faculty Working in Quality Improvement and Patient Safety
Bibliographic record
Abstract
Academic clinical departments have the opportunity and responsibility to improve the quality and value of care and patient safety by supporting effective quality improvement activities. The pressure to provide high-value care while further developing academic programs has increased the complexity of decision making and change management in academic health systems. Overcoming these challenges will require faculty engagement and leadership; however, most academic departments do not have a sufficient number of individuals with expertise and experience in quality improvement and patient safety (QI/PS). Accordingly, the authors of this article advocate for a targeted and proactive approach to developing faculty working in QI/PS. They propose a strategy predicated on the identification of QI/PS as a strategic priority for academic departments, the creation of enabling resources in QI/PS, and the expansion of rigorous training programs in change management and in improvement and implementation sciences. Professional organizations, health systems, medical schools, and academic departments should recognize successful QI/PS work with awards and promotions. Individual faculty members should expand their collaborative networks, consider the generalizability and scholarly impact of their efforts when designing QI/PS initiatives, and benchmark the outcomes of their performance. Appointments and promotions committees should work proactively with department and QI/PS leaders to ensure that outstanding achievement in QI/PS is defined and recognized. As with the development of physician-investigators and clinician-educators, departments and health systems need a comprehensive approach to support and recognize the contributions of faculty working in QI/PS to meet the considerable needs and opportunities in health care.
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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 | 0.055 | 0.059 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.021 | 0.013 |
| Open science | 0.006 | 0.024 |
| Research integrity | 0.014 | 0.015 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".