Physician group, physician and patient characteristics associated with joining interprofessional team-based primary care in Ontario, Canada
Bibliographic record
Abstract
PURPOSE: Countries throughout the world have been experimenting with new models to deliver primary care. We investigated physician group, physician and patient characteristics associated with voluntarily joining team-based primary care in Ontario. METHODS: This cross-sectional study linked provincial administrative datasets to form data extractions of interest over time with the earliest in 2005 and the latest in 2013. We generated mixed, generalized chi-square and multivariate models to compare the characteristics of teams and non-teams, both with blended capitation reimbursement, and to examine characteristics associated with joining a team. RESULTS: Having more physicians per group, being a female physician, having more years under the blended capitation model, having more patients in the lowest income quintile and more patients residing in rural areas were positively associated with joining a team. Being a female physician and having more patients who are males, recent immigrants and living in rural areas were positively associated with the outcome of joining teams in the late phase. CONCLUSIONS: Our study findings indicate that there are differences in physician group, physician and patient characteristics when comparing teams to non-teams. Other jurisdictions aiming to expand physician participation in interprofessional care should note those factors. Researchers looking to understand the impact of team-based care should be aware of pre-existing differences and the need to address selection bias associated with participation in team-based care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".