Regional variation in primary care improvement strategies and policy: case studies that consider qualitative contextual data for performance measurement in three Canadian provinces
Bibliographic record
Abstract
OBJECTIVE: To explore regional primary care improvement strategies that are potentially determinants of primary care performance. DESIGN: Multiple comparative embedded case study. SETTING: Three regions in Canada: Fraser East, British Columbia; Eastern Ontario Health Unit, Ontario; Central Zone, Nova Scotia. DATA SOURCES: (1) In-depth interviews with purposively selected key informants (eg, primary care decision-makers, physician leads, regulatory agencies) and focus groups with patients and clinicians (n=68 participants) and (2) published and grey literature (n=205 documents). OUTCOME MEASURES: Variations in spread and uptake of primary care improvement strategies across the three study regions. NVivo (V.11) was used to manage data and perform content analysis to identify categories within and across cases. The coding structure was developed by researchers through iterative collaboration, using inductive and deductive processes. RESULTS: Six overarching primary care improvement strategies, differing in focus and spread, were implemented across the three study regions: interprofessional team-based approaches, provider skill mix expansion, physician groups and networks, information systems, remuneration and performance measurement and reporting infrastructure. CONCLUSION: The addition of information on regional improvement strategies to primary care performance reports could add important contextual insights into primary care performance results. This could help identify possible drivers of reported performance outcomes and levers for change in practice, regional and system-level settings.
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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 | 0.013 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.015 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".