Harnessing the Power of Residents as Change Agents in Quality Improvement
Bibliographic record
Abstract
Residency training represents a unique period when learners begin to personally experience the patient safety and quality-of-care issues that affect health care systems and increasingly take responsibility to address them. Their integration into the clinical workflow in clinics, wards, and operating rooms positions them perfectly to observe and characterize the underlying processes that contribute to patient safety and health care quality problems. Residents' practices and perspectives are less entrenched than those of their faculty counterparts, which enables them to offer fresh ideas on the quality improvement (QI) process. Their creativity and ingenuity serve as assets when coming up with new and innovative changes to test using rapid change cycles. As such, they are ideally suited to serve as health systems change agents. Training programs and clinical institutions typically see residents as frontline care providers whose primary role is to treat the patient in front of them. Yet, by enabling residents to "treat the system" through QI work, they can take on the role of residents as change agents, which has the potential to have long-lasting effects on patient care on a much wider scale. However, training programs must do more than simply harness residents' enthusiasm and root them on from the sidelines. Instead, they must create an environment that is conducive to successfully implementing changes at the curricular, institutional, and health systems levels.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.012 |
| 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".