How Successful Are Residents and Fellows at Quality Improvement?
Bibliographic record
Abstract
BACKGROUND: Nationally, there is an expectation that residents and fellows participate in quality improvement (QI), preferably interprofessionally. Hospitals and educators invest time and resources in projects, but little is known about success rates or what fosters success. PURPOSE: To understand what proportion of trainee QI projects were successful and whether there were predictors of success. METHODS: We examined resident and fellow QI projects in an integrated healthcare system that supports diverse training programs in multiple hospitals over 2 years. All projects were reviewed to determine whether they represented actual QI. Projects determined as QI were considered completed or successful based on QI project sponsor self-report. Multiple characteristics were compared between successful and unsuccessful projects. RESULTS: Trainees submitted 258 proposals, of which 106 (41.1%) represented actual QI. Non-QI projects predominantly represented needs assessments or retrospective data analyses. Seventy-six percent (81/106) of study sponsors completed surveys about their projects. Less than 25% of projects (59/258) represented actual QI and were successful. Project category was predictive of success, specifically those aimed at preventive care or education. CONCLUSION: Less than a quarter of trainee QI projects represent successful QI. IMPLICATIONS: Hospitals and training programs should identify interventions to improve trainee QI experience.
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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 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".