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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".