How can rural community-engaged health services planning achieve sustainable healthcare system changes?
Bibliographic record
Abstract
OBJECTIVES: The objectives of the Rural Site Visit Project (SV Project) were to develop a successful model for engaging all 201 communities in rural British Columbia, Canada, build relationships and gather data about community healthcare issues to help modify existing rural healthcare programs and inform government rural healthcare policy. DESIGN: An adapted version of Boelen's health partnership model was used to identify each community's Health Care Partners: health providers, academics, policy makers, health managers, community representatives and linked sectors. Qualitative data were gathered using a semistructured interview guide. Major themes were identified through content analysis, and this information was fed back to government and interviewees in reports every 6 months. SETTING: The 107 communities visited thus far have healthcare services that range from hospitals with surgical programs to remote communities with no medical services at all. The majority have access to local primary care. PARTICIPANTS: Participants were recruited from the Health Care Partner groups identified above using purposeful and snowball sampling. PRIMARY AND SECONDARY OUTCOME MEASURES: A successful process was developed to engage rural communities in identifying their healthcare priorities, while simultaneously building and strengthening relationships. The qualitative data were analysed from 185 meetings in 80 communities and shared with policy makers at governmental and community levels. RESULTS: 36 themes have been identified and three overarching themes that interconnect all the interviews, namely Relationships, Autonomy and Change Over Time, are discussed. CONCLUSION: The SV Project appears to be unique in that it is physician led, prioritises relationships, engages all of the healthcare partners singly and jointly in each community, is ongoing, provides feedback to both the policy makers and all interviewees on a 6-monthly basis and, by virtue of its large scope, has the ability to produce interim reports that have helped inform system change.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".