Those Left Behind From Voluntary Medical Home Reforms in Ontario, Canada
Bibliographic record
Abstract
PURPOSE: Health systems are transitioning patients to medical homes to improve health outcomes and reduce cost. We sought to understand the characteristics and quality of care for patients who did and did not participate in the voluntary transition to medical homes. METHODS: We used administrative data for diabetes monitoring and cancer screening to compare services received by patients attached to a medical home (n = 10,785,687) with services received by those seeing a fee-for-service physician (n = 1,321,800) in Ontario, Canada, on March 31, 2011. We used Poisson regression to examine associations in 2011 after adjustment for patient factors and also assessed changes in outcomes between 2001 and 2011. RESULTS: Patients attached to a fee-for-service physician were more likely to be immigrants and live in a low-income neighborhood and urban area. They were less likely to receive recommended testing for diabetes (25% vs 34%; adjusted relative risk [RR] = 0.74; 95% CI, 0.73-0.75) and less likely to receive screening for cervical (52% vs 66%; adjusted RR = 0.79; 95% CI, 0.79-0.79), breast (58% vs 73%; adjusted RR = 0.80; 95% CI, 0.80-0.81), and colorectal cancer (44% vs 62%; adjusted RR = 0.72; 95% CI, 0.71-0.72) compared with patients attached to a medical home physician in 2011. These differences in quality of care preceded medical home reforms. CONCLUSION: Patients left behind from medical home reforms are more likely to be poor, urban, and new immigrants and receive lower quality care. Strategies are needed to reach out to these patients and their physicians to reduce gaps in 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".