Relational Continuity, Physician Payment, and Team-Based Primary Care in the Canadian Health Care System
Bibliographic record
Abstract
PURPOSE: Continuity is a core component of primary care and known to differ by patient characteristics. It is unclear how primary care physician payment and organization are associated with continuity. METHODS: We analyzed administrative data from 7,110,036 individuals aged 16+ in Ontario, Canada who were enrolled to a physician and made at least 2 visits between October 1, 2017 and September 30, 2019. Continuity with physician and practice group was quantified using the usual provider of care index. We used log-binomial regression to assess the relationship between enrollment model and continuity adjusting for patient characteristics. RESULTS: Mean physician and group continuity were 67.3% and 73.8%, respectively, for patients enrolled in enhanced fee-for-service, 70.7% and 76.2% for nonteam capitation, and 70.6% and 78.7% for team-based capitation. These differences were attenuated in regression models for physician-level continuity and group-level continuity. Older age was the most notable factor associated with continuity. Compared with those 16 to 34, those 80 and older had 1.45 times higher continuity with their physician. CONCLUSION: Our results suggest that continuity does not differ substantially by physician payment or organizational model among primary care patients who are formally enrolled with a physician in a setting with universal health insurance.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".