A cross‐sectional study of community‐level physician retention and hospitalization in rural Ontario, Canada
Bibliographic record
Abstract
PURPOSE: Many rural communities experience poor family physician retention. We examined the association between community-level physician retention and hospitalization for all causes and ambulatory care-sensitive conditions (ACSCs) in 2017 among residents of rural communities in Ontario, Canada. METHODS: We conducted a population-based cross-sectional study by linking administrative data from the public health insurance program in Ontario. To create the physician retention measure for each rural community, we divided the number of family physicians who worked in the community in both 2016 and 2017 by the total number of unique family physicians in the community in either year. We grouped retention level by tertile and added a fourth category, "no physician" to include communities that did not have any residing physicians in either 2016 or 2017. Outcomes were all-cause hospitalization and ACSC hospitalization between April 1, 2017 and March 31, 2018. FINDINGS: Among 1,436,794 rural residents, there were 93,752 all-cause hospitalizations and 8,691 ACSC hospitalizations in 2017. After controlling for other predictors, compared to residents in low-retention communities, residents of medium- and high-retention communities were 0.888 (95% CI: 0.868-0.909) and 0.937 (95% CI: 0.915-0.960) times as likely to have all-cause hospitalization, and residents of high-retention communities were 0.918 (95% CI: 0.858-0.981) times as likely to have ACSC hospitalization in 2017. CONCLUSIONS: Community-level physician retention is significantly associated with all cause and ACSC hospitalization in rural communities in Ontario, and may serve as an alternate measure to assess the impact of disrupted continuity of 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".