System interventions to support rural access to maternity care: an analysis of the rural surgical obstetrical networks program
Bibliographic record
Abstract
Abstract Background The Rural Surgical Obstetrical Networks (RSON) project was developed in response to the persistent attrition of rural maternity services across Canada over the past two decades. While other research has demonstrated the adverse health and psychosocial consequences of losing local maternity services, this paper explores the impact of a program designed to increase the sustainability of rural services themselves, through the funding of four “pillars”: increased scope and volume, clinical coaching, continuous quality improvement (CQI) and remote presence technology. Methods We conducted in-depth, qualitative research interviews with rural health care providers and administrators in eight rural communities across British Columbia to understand the impact of the RSON program on maternity services. Researchers used thematic analysis to generate common themes across the dataset and interpret findings. Findings Participants articulated six themes regarding the sustainability of maternity care as actualized through the RSON project: safety and quality through quality improvement opportunities, improved access to care through increased surgical volume and OR backup, optimized team function through innovative models of care, improved infrastructure, local innovation surrounding workforce shortages, and locally tailored funding models. Conclusion Rural maternity sites benefited from the funding offered through the RSON pillars, as demonstrated by larger volumes of local deliveries, nearly unanimous positive accounts of the interventions by health care providers, and evidence of staffing stability during the study time frame. As such, the interventions provided through the Rural Surgical Obstetrical Networks project as well as study findings on the common themes of sustainable maternity care should be considered when planning core rural health services funding schemes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.045 | 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".