Physician recruitment and retention in rural and underserved areas
Bibliographic record
Abstract
PURPOSE: The purpose of this paper is to identify the challenges when recruiting and retaining rural physicians and to ascertain methods that make rural physician recruitment and retention successful. There are studies that suggest rural roots is an important factor in recruiting rural physicians, while others look at rural health exposure in medical school curricula, self-actualization, community sense and spousal perspectives in the decision to practice rural medicine. DESIGN/METHODOLOGY/APPROACH: An extensive literature review was performed using Academic Search Complete, PubMed and The Cochrane Collaboration. Key words were rural, rural health, community hospital(s), healthcare, physicians, recruitment, recruiting, retention, retaining, physician(s) and primary care physician(s). Inclusion criteria were peer-reviewed full-text articles written in English, published from 1997 and those limited to USA and Canada. Articles from foreign countries were excluded owing to their unique healthcare systems. FINDINGS: While there are numerous articles that call for special measures to recruit and retain physicians in rural areas, there is an overall dearth. This review identifies several articles that suggest recruitment and retention techniques. There is a need for a research agenda that includes valid, reliable and rigorous analysis regarding formulating and implementing these strategies. ORIGINALITY/VALUE: Rural Americans are under-represented when it comes to healthcare and what research there is to assist recruitment and retention is difficult to find. This paper identify the relevant research and highlights key strategies.
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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 | 0.020 | 0.087 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".