The Relationship between Primary Care Models and Processes of Diabetes Care in Ontario
Bibliographic record
Abstract
This study examined the association between Ontario's differing primary care models and receipt of recommended testing for people with diabetes. We analyzed available administrative data for 757 928 people with diabetes aged 40 years and older. We assigned them to a primary care physician and assessed whether they had received 3 key monitoring tests between 2006 and 2008. We used multivariable generalized estimating equation models to test the associations among various primary care models and receipt of recommended testing. Ontarians with diabetes who were enrolled in a non-team blended capitation model (OR 1.18, 95% CI 1.09 to 1.27) and those enrolled in a team-based blended capitation model (OR 1.20, 95% CI 1.13 to 1.28) were more likely than those enrolled in a blended fee-for-service model to receive the optimal number of 3 recommended monitoring tests. Patients who were not enrolled in any model and who were assigned to a traditional fee-for-service physician were least likely to receive optimal monitoring compared to those enrolled in a blended fee-for-service model (OR 0.60, 95% CI 0.57 to 0.62). The biggest gap in diabetes care was for patients not enrolled in any primary care model. Research and policy work is needed to understand and reduce this care gap, especially which provider and patient-level factors are involved. Options may include intensive outreach to patients, knowledge translation to physicians, encouraging enrollment and efforts to remove barriers to 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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".