Bibliographic record
Abstract
Introducao: Introduction: Despite a number of Canadian studies linking rural background and rural placements to rural practice, there remains variation within this literature with regard to what constitutes “rural.” A consistent definition of “rural” may be a useful tool for medical schools, governments, and other interested stakeholders as policies and programs to increase rural practice are created. Objetivos: Objective: This study reviews selected Canadian research articles that include an operational definition of rural. Our aim is suggest a definition of rural that could be applied nationally and consistently. Metodologia ou Descricao da Experiencia: Methodology: From July to August 2013 PubMed was used to identify Canadian-based research articles that: 1) utilized a clearly operationalized definition of ‘rural’ to describe or categorize medical student background, educational placements, or physician practice location 2) tested for a statistical relationship between rural background or rural educational placements and rural practice locations. Search terms included (but were not limited to): rural background, rural origin, rural placement, rural education, rural doctors, rural physicians. Abstracts were reviewed; articles that fit criteria 1) and 2) above were subjected to full review and definitions of rural were categorized. Resultados: Results: In all, 14 articles were identified for inclusion in this review. Among these articles, definitions of “rural” used included: community of under 10,000 population; community of under 25,000; community of under 100,000; provincially specific combinations of size and distance from a larger center; and self-perception (e.g. self-perception of community of practice location as ‘rural’). A table was produced to show the frequency with which each definition was used and the statistical relationships identified by the studies under review. Conclusao ou Hipoteses: Conclusions: This review points to the usefulness of standard and graduated definitions of rural. We argue for the adoption of operational approach that defines communities of under 25,000 as rural, with a major subgroup of those communities of under 10,000, but recognizes gradations of rural from under population under 1000 to population under 100000.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.029 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".