The Organisational Infrastructure of a Canadian Rural Health Network: A Four‐Year Longitudinal Survey Study
Bibliographic record
Abstract
BACKGROUND: Formal networks are increasingly being used as a strategy to address complex health system issues. This study aimed to understand the organisational performance of a novel network, the Rural Surgical and Obstetrical Networks (RSON) in the Canadian province of British Columbia, as it developed and grew over four years. METHODS: Between 2019 and 2022, we administrated an annual 37-item survey on network organisational aspects with RSON leaders. We calculated the percentage of favourable ratings (four or five rating out of five) for each survey item and used a two-tailed Wilcoxon Mann-Whitney rank sum test to compare ratings over time. Key themes in respondent comments were described narratively. RESULTS: Over four years, we distributed 114 survey invitations to RSON leaders and received 77 responses. From 2019 to 2022, 24 out of 37 survey items (65%) had a statistically significant increase in ratings. Ratings and comments indicated that RSON could have improved its function by (a) including more peripheral network members in decision-making and (b) formalising structures and processes for some network areas. Findings also indicate the presence of three network tensions within RSON: inclusiveness versus efficiency, stability versus flexibility, and network operations versus health system operations. CONCLUSION: Study findings validate and build on existing network theories and provide practical learnings for other jurisdictions interested in implementing a network like RSON. Among the tensions identified within RSON, the network operations versus health system operations tension, specific to a healthcare delivery setting, has not been well described previously.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.007 | 0.001 |
| 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".