A Cross-sectional Study of Community-level Physician Retention and Diabetes Management in Rural Ontario
Bibliographic record
Abstract
OBJECTIVE: Our aim in this study was to determine the impact of community-level physician retention on the quality of diabetes care in rural Ontario. METHODS: Using administrative data, we compared diabetes quality of care. We defined retention as the proportion of physicians in a community from one year to the next. We grouped retention level by tertile and added a category for those communities with no physician. RESULTS: Residents of high-retention communities were more likely to have had glycated hemoglobin (odds ratio [OR], 1.10; 95% confidence interval [CI], 1.06 to 1.14) and low-density lipoprotein (OR, 1.17; 95% CI, 1.13 to 1.22) testing, but less likely to have had testing for urine albumin-to-creatine ratio (OR, 0.86; 95% CI, 0.83 to 0.89) or to have received an angiotensin-converting enzyme inhibitor or angiotensin-2 receptor blocker (OR, 0.91; 95% CI, 0.86 to 0.95) or a statin (OR, 0.91; 95% CI, 0.87 to 0.96), when compared with low-retention communities. Communities with no residing physician had care that was equivalent to or better than that in high-retention communities. CONCLUSIONS: Community-level physician retention, based on a 2-year time frame, was significantly related to quality of diabetes care. A closer look at models of care in communities with no residing physician is warranted. Community-level physician retention can be used to assess the impact of physician shortages on diabetes management in rural communities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".