Recruitment and retention of general practitioners in rural Canada and Australia: a review of the literature.
Bibliographic record
Abstract
INTRODUCTION: Both Canada and Australia are facing severe shortages of primary health workers, and these shortages are exacerbated in rural and remote communities. This literature review highlights similarities and explores the factors that serve to attract and retain family practitioners in underserved regions of Canada and Australia. METHODS: We used MEDLINE on OvidSP to review the literature between Jan. 1, 2000, and June 30, 2012. We excluded sources if the primary objective did not consider recruitment or retention of general practitioners. RESULTS: We found a total of 114 sources, 28 of which were excluded, leaving 86 sources for review. We organized results according to 5 life stages of family physicians in rural practice and graded the literature according to the strength of the methodology and the relevance of the findings. We chronologically categorized Canadian and Australian literature that discussed recruitment and retention of family practitioners into rural practice. CONCLUSION: Various factors that pertain to each life stage of a family physician have been shown to positively correlate with the eventual decision to commence and remain practising in rural areas. Training programs should be better structured to attract candidates who are more likely to enter rural practice. Policy-makers should be mindful of these findings, because improvements in retention will deliver large financial savings.
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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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".