Different practice patterns of rural and urban general practitioners are predicted by the General Practice Rurality Index.
Bibliographic record
Abstract
INTRODUCTION: There are differences between rural and urban general medical practice. However, research in this area has been hampered by the lack of a practical and valid definition of "rural." This study attempts to validate the General Practice Rurality Index (GPRI) by showing that it can predict the fee-for-service billing patterns of general practitioners in British Columbia. METHODS: We obtained one year of fee-for-service billing data for all general practitioners in BC, apportioned by local health area (LHA). The total numbers of each type of service in each LHA were categorized into logical groups and expressed as a percentage of total services for that LHA. Each LHA was given a full GPRI score and a simplified GPRI (GPRI-S) score. We then compared the scores and percentage of services in each fee category. RESULTS: We found significant correlations between the degree of rurality and the percentage of certain services. The GPRI-S produced more significant correlations than the full GPRI. CONCLUSIONS: This study provides evidence that both the full GPRI and a simplified version can be used to predict practice patterns of BC general practitioners. Further study is needed to prove whether either of these indices will be an accurate and reliable measure of rurality across Canada.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".