Physician recruitment and retention in rural and underserved areas
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it