Determinants of Hospital-based Physician Participation in Quality Improvement: A Survey of Hospitalists in British Columbia, Canada
Bibliographic record
Abstract
Objective: We aimed to understand the extent of hospitalist involvement in system improvement efforts across the province of British Columbia in Canada and provide insights into determinants of such participation. Materials and Methods: We designed a web-based survey and asked about individual, programmatic, and institutional characteristics that may facilitate or impair hospitalist involvement in quality improvement (QI) activities. The survey was sent to all individuals who participated in "hospitalist care" from January 2014 to February 2015, in the province of British Columbia, Canada. We conducted both quantitative and qualitative analysis of responses. Results: We received 57 complete responses to the survey of 322 invited individuals (17.7% response rate). Of these, 15 individuals (26.3%) indicated that they had participated in QI initiatives. Respondents highlighted high clinical workload and lack of time, lack of QI skills and training, lack of access to performance data, poor support from hospital/health authority administration, and lack of financial compensation as main barriers to QI involvement. These themes were also supported in logistic regression, where QI training and the number of weeks worked as a hospitalist showed significant predictive properties for involvement in QI initiatives. Conclusion: Our study attempts to understand the various individual or organizational attributes that could facilitate involvement by hospital-based generalist physicians in QI activities. Our findings show lack of formal QI training is an important barrier for hospitalist involvement in QI, and highlight the need for formal training, dedicated time, support from physician leadership, and financial incentive as important facilitators for participation in systemic improvement efforts.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".