'Rural' doesn't mean 'uniform': northern vs southern rural family physicians' workload and practice structures in Ontario
Bibliographic record
Abstract
INTRODUCTION: There is a tendency in health policy in Ontario, Canada, to conflate 'northern' with 'rural' and to equate northern rural settings with southern ones. Although previous research has identified some differences between rural and urban practitioners, these studies have not acknowledged the subtle nuances that make rural practice different in the north than in the south. This study looks more closely at practice patterns and compares number of hours worked per week, patient volume and practice type for rural northern, rural southern, urban northern and urban southern physicians. METHODS: This study utilized data from Ontario's medical regulatory authority's 2007 annual membership renewal survey. Descriptive statistics and χ(2) analyses were used to examine practice type (eg solo, clinical group), hours worked per week and number of patient visits per week for 10 968 primary care physicians in Ontario's rural north, rural south, urban north and urban south. RESULTS: Three key results emerged from the analyses: (1) physicians in rural northern Ontario worked more hours per week than their counterparts in other regions of the province, yet (2) they saw fewer patients per week, and (3) worked more frequently in clinical group-based practices. CONCLUSIONS: Rural northern physicians with different practice structures, different patient types, broader scope of services, and different encounter lengths indicate variations specific to locations and populations and communities. The interaction between the rural and northern context is unique and as such a blanket 'rural' or 'northern' approach to policy development is likely to be ineffective.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".