The impact of rural rotations on urban based postgraduate learners: A literature review
Bibliographic record
Abstract
Background/Objective: The rural physician shortage remains an international crisis. Rural rotations are commonly used to address the issue. This review assesses the published evidence of the impact of rural rotations on urban-based postgraduate learners.Methods: The OVID Medline database was searched for eligible articles published in peer-reviewed academic journals between 1980 and 2017. Data were extracted and analyzed to draw inferences about the impact of rural rotations on urban-based postgraduate learners. The methodological quality of included articles was assessed with the Medical Education Research Study Quality Instrument (MERSQI).Results: The search identified 301 articles; 19 studies met inclusion criteria (mean MERSQI score 11.95). Of the various rural rotation characteristics reported, duration was most consistently associated with the eventual rural practice. No consensus of impact was found for other characteristics. Our review provided indications of the cumulative effect of the postgraduate rural rotation, rural origin, and rural intent on rural practice decisions.Conclusions: The importance of rural rotations during urban postgraduate training for the outcome of rural practice is apparent. However, the reliance of medical educational systems on the rural rotation, specifically duration, does not accurately reflect the complexity of the choice to practice in a rural community.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".