Characteristics of physicians and patients who join team-based primary care practices: Evidence from Quebec's Family Medicine Groups
Bibliographic record
Abstract
PURPOSE: New models of delivering primary care are being implemented in various countries. In Quebec, Family Medicine Groups (FMGs) are a team-based approach to enhance access to, and coordination of, care. We examined whether physicians' and patients' characteristics predicted their participation in this new model of primary care. METHODS: Using provincial administrative data, we created a population cohort of Quebec's vulnerable patients. We collected data before the advent of FMGs on patients' demographic characteristics, chronic illnesses and health service use, and their physicians' demographics, and practice characteristics. Multivariate regression was used to identify key predictors of joining a FMG among both patients and physicians. RESULTS: Patients who eventually enrolled in a FMG were more likely to be female, reside outside of an urban region, have a lower SES status, have diabetes and congestive heart failure, visit the emergency department for ambulatory sensitive conditions and be hospitalized for any cause. They were also less likely to have hypertension, visit an ambulatory clinic and have a usual provider of care. Physicians who joined a FMG were less likely to be located in urban locations, had fewer years in medical practice, saw more patients in hospital, and had patients with lower morbidity. CONCLUSIONS: Physicians' practice characteristics and patients' health status and health care service use were important predictors of joining a FMG. To avoid basing policy decisions on tenuous evidence, policymakers and researchers should account for differential selection into team-based primary health care models.
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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 | 0.003 | 0.015 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".