Partnering for change
Bibliographic record
Abstract
PURPOSE: Despite many calls to strengthen connections between health systems and communities as a way to improve primary healthcare, little is known about how new collaborations can effectively alter service provision. The purpose of this paper is to explore how a health authority, municipal leaders and physicians worked together in the process of transforming primary healthcare. DESIGN/METHODOLOGY/APPROACH: A longitudinal qualitative case study was conducted to explore the processes of change at the regional level and within seven communities across Northern British Columbia (BC), Canada. Over three years, 239 interviews were conducted with physicians, municipal leaders, health authority clinicians and leaders and other health and social service providers. Interviews and contextual documents were analyzed and interpreted to articulate how ongoing transformation has occurred. FINDINGS: Four overall strategies with nine approaches were apparent. The strategies were partnering for innovation, keeping the focus on people in communities, taking advantage of opportunities for change and encouraging experimentation while managing risk. The strategies have bumped the existing system out of the status quo and are achieving transformation. Key components have been a commitment to a clear end-in-view, a focus on patients, families, and communities, and acting together over time. ORIGINALITY/VALUE: This study illuminates how partnering for primary healthcare transformation is messy and complicated but can create a foundation for whole system change.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.030 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.019 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.003 | 0.029 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.024 | 0.006 |
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".