Rural general practitioners have different personal and professional trajectories from those of their urban colleagues: a case-control study
Bibliographic record
Abstract
BACKGROUND: In France, rural general practitioner (GP) numbers could reduce by 20% between 2006 and 2030 if no measures are taken to address primary care access difficulties. In countries such as Australia, the USA and Canada, intrinsic and extrinsic factors associated with GPs practising in rural areas include rural upbringing and rural training placements. However, the health systems and rural area definition differ between these countries and France making result extrapolation difficult. These factors must be studied in the context of the French heath system, to design strategies to improve rural GP recruitment and retention. This study aims to identify the intrinsic and extrinsic factors associated with GPs practising in rural areas in France. METHODS: This case-control study was conducted between May and September 2020. Included GPs practised in Brittany, France, and completed a self-administered questionnaire. The cases were rural GPs and controls were urban GPs. National references defined rural and urban areas. Comparisons between rural and urban groups were conducted using univariate and multivariate analyses to identify factors associated with practising in a rural area. RESULTS: The study included 341 GPs, of which 146 were in the rural group and 195 in the urban group. Working as a rural GP was significantly associated with having a rural upbringing (OR = 2.35; 95% CI [1.07-5.15]; p = 0.032), completing at least one undergraduate general medicine training placement in a rural area (OR = 3.44; 95% CI [1.18-9.98]; p < 0.023), and having worked as a locum in a rural area for at least three months (OR = 3.76; 95% CI [2.28-6.18]; p < 0.001). Choosing to work in a rural area was also associated with the place of residence at the end of postgraduate training (OR = 5.13; 95% CI [1.38-19.06]; p = 0.015) and with the spouse or partner having a rural upbringing (OR = 2.36; 95% CI [1.12-4.96]; p = 0.023) or working in a rural area (OR = 5.29; 95% CI [2,02-13.87]; p < 0.001). CONCLUSIONS: French rural GPs were more likely to have grown up, trained, or worked as a locum in a rural area. Strategies to improve rural GP retention and recruitment in France could therefore include making rural areas a more attractive place to live and work, encouraging rural locum placements and compulsory rural training, and possibly enrolling more medical students with a rural background.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".