A mixed-methods descriptive study on the role of continuous quality improvement in rural surgical and obstetrical stability: Considering enablers, challenges and impact
Bibliographic record
Abstract
INTRODUCTION: The Rural Surgical Obstetrical Networks (RSON) initiative in BC was developed to stabilize and grow low volume rural surgical and obstetrical services. One of the wrap-around supportive interventions was funding for Continuous Quality Improvement (CQI) initiatives, done through a local provider-driven lens. This paper reviews mixed-methods findings on providers' experiences with CQI and the implications for service stability. BACKGROUND: Small, rural hospitals face barriers in implementing quality improvement initiatives due primarily to lack of resource capacity and the need to prioritize clinical care when allocating limited health human resources. Given this, funding and resources for CQI were key enablers of the RSON initiative and seen as an essential part of a response to assuaging concerns of specialists at higher volume sites regarding quality in lower volume settings. METHODS: Data were derived from two datasets: in-depth, qualitative interviews with rural health care providers and administrators over the course of the RSON initiative and through a survey administered at RSON sites in 2023. FINDINGS: Qualitative findings revealed participants' perceptions of the value of CQI (including developing expanded skillsets and improved team function and culture), enablers (the organizational infrastructure for CQI projects), challenges in implementation (complications in protecting/prioritizing CQI time and difficulty with staff engagement) and the importance of local leadership. Survey findings showed high ratings for elements of team function that relate directly to CQI (team process and relationships). CONCLUSION: Attention to effective mechanisms of CQI through a rural lens is essential to ensure that initiatives meet the contextual realities of low-volume sites. Instituting pathways for locally-driven quality improvement initiatives enhances team function at rural hospitals through creating opportunities for trust building and goal setting, improving communication and increasing individual and team-wide motivation to improve patient care.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".