Optimizing rural healthcare through improved team function: a case study of the Rural Surgical Obstetrical Networks programme
Bibliographic record
Abstract
We explored enablers and mechanisms of optimal team function within rural hospital teams, and the impact of these factors on health service sustainability in British Columbia. The data were drawn from interviews and focus groups with healthcare providers and administrators (n = 169) who participated in the Rural Surgical Obstetrical Networks (RSON) initiative to support low-volume rural surgical and obstetrical services in British Columbia, Canada. The 5-year programme (2018–2022) provided evidence-based system interventions across eight rural sites with the objective of providing sustainable, quality health services to meet population needs. To explore the impact of RSON interventions on local team function, we performed a scoping review, to assess the current literature surrounding enablers of effective rural hospital teamwork. Through inductive thematic analysis of interview data, we identified five enablers of good team function at RSON sites, including emphasis on local leadership, shared direction, commitment to sustainability, respect and solidarity among colleagues, and meaningful communication. The RSON project led to a shift in team culture in participating sites, improved team function, and contributed to improved clinical processes and patient outcomes. The findings have implications for rural health policy and practice in British Columbia and other jurisdictions with similar health service delivery models and geographic contexts.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".