A medical GIS approach to defining rural for medical education research and policy
Bibliographic record
Abstract
Background While medical education researchers have acknowledged the difficulty of developing a definition of rural, authors have also suggested that definitions of rural should be suited to their research purpose (du Plessis et al. 2001, Couper 2003). Objectives One component of the Learners and Locations project undertaken at the Health Research Unit at MUN focused on developing a definition of rural for medical education research. In this paper, we describe the L & L community classification system and compare it to the Statistical Area Classification (SAC) categories developed by Statistics Canada. Methods Statistics Canada and Canadian National Road Network data, along with the GIS software ArcGIS, was used to divide Newfoundland and Labrador communities into the 11 categories developed in the L & L project: Metropolis (Pop. > 1,000,000); Very Large City (Pop. 500,000–1,000,000); Large City (Pop. 100,000–500,000); Medium City (Pop. 50,000–100,000); Small City (Pop. 10,000–50,000, 500km); Rural Close Community (Pop.
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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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".