Informing family physician payment reform in Canada: protocol for a cross-provincial, multimethod study
Bibliographic record
Abstract
BACKGROUND: Amid growing concerns about primary care accessibility and the need to support longitudinal, community-based models of care, Canadian provinces have implemented major reforms to how family physicians are paid. These models share objectives of making longitudinal, community-based family practice more attractive and, to some degree, addressing long-standing disparities in pay between family medicine and other specialties. These new remuneration models require robust evaluation to guide improvements, future investments and planning. METHODS AND ANALYSIS: We will conduct a multimethod study to explore physician perceptions and outcomes of these new models. First, we will complete semi-structured interviews with family physicians in British Columbia, Manitoba and Nova Scotia (provinces where a new blended compensation model has been introduced). Interviews will explore family physicians' motivations for moving onto the blended compensation model; how the model has impacted their practice, administrative burden, visit length, capacity, changes to care coordination; and other areas of interest. Second, using provincial and national administrative datasets, we will assess the impact of these payment reforms on service volume, attachment/enrolment, continuity of care, and costs. ETHICS AND DISSEMINATION: We have obtained cross-jurisdictional ethics approvals from Research Ethics British Columbia for the qualitative components and Nova Scotia Health for the quantitative components of this research. Harmonised ethics approvals have been obtained from additional institutions across all study regions. We will create summaries of findings of provincial and cross-provincial analyses and share them with relevant policymakers, physician associations and study participants. Our dissemination will also include traditional publications such as peer-reviewed articles, commentaries/editorials, and academic conferences.
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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 | 0.064 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.059 | 0.008 |
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".