Physician engagement in quality improvement: A cross-sectional study
Bibliographic record
Abstract
Objective: The positive impact of quality improvement (QI) on organizational and system outcomes has the potential to contribute to a high-performing health system. Physician engagement in QI has been linked to the success and sustainability of improvement initiatives. An informed overview of physicians’ interests in QI, opportunities to be involved in QI efforts, and insights into physicians’ experiences of participation, both in hospital and general practice is critical to understanding the challenges and opportunities for physician engagement in QI. The purpose of this study was to gain insight into both the number of physicians currently trained and participating in QI and identify key barriers preventing physicians from being trained and participating in QI.Methods: A cross-sectional online survey was used to evaluate physician engagement in QI. A total of 231 physicians across Ontario, Canada, participated in the study.Results: Results indicate that leadership should continue to make Quality Improvement (QI) training opportunities available to physicians.Conclusions: If more physicians are to be engaged in QI, there is a need to clearly identify and communicate opportunities for QI projects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".