Rural health career pathways: research themes in recruitment and retention
Bibliographic record
Abstract
Objective. This paper describes stages in the research literature related to recruitment and retention of health professionals to rural health careers. Data sources. Electronic databases accessed included Medline, CINAHL, Social Sciences and Humanities. Key search terms included ‘high school’, ‘career choices’, ‘rural’, ‘attachment’, ‘recruitment’ and ‘retention’. Data synthesis. We identified four stages: (1) making career choices; (2) being attached to place; (3) taking up rural practice; and (4) remaining in rural practice. This is termed the ‘rural pipeline’. However, as some stages of the ‘rural pipeline’ refer specifically to the medical profession, we propose an extension of the notion of the medical ‘rural pipeline’ to include other professions such as nursing, midwifery and allied health. Conclusions. Utilising the ‘rural pipeline’ as a template for medicine, nursing and allied health would strengthen current approaches to the recruitment and retention of professionals in rural areas and provide a consolidated evidence base that would assist in policy development to improve availability and service provision of the rural health workforce. Future research that utilises a multidisciplinary approach could explore how the role and relationship between place and identity shape needs of career choices and would provide important information to advance the practical aspects supporting rural health career pathways. What is known about the topic? A universal shortage of rural health professionals is a significant issue and is becoming critical in rural areas of Australia. Although there have been many studies, internationally and in Australia, there are several gaps in recruitment and retention of rural health professionals that require further attention. What does this paper add? This paper examines workforce studies related to recruitment and retention of health professionals to rural health careers. The pipeline, however, refers mainly to the medical profession. The stages in this paper extend the notion of the medical ‘rural pipeline’ to include other professions such as nursing and allied health. This paper focusses on literature concerning developed countries such as Australia, New Zealand, Europe, the USA and Canada and identifies several proposed areas of future research. What are the implications for practitioners? The literature clearly identifies important issues for the rural health workforce. Having an understanding of the key issues underpinning the recruitment and retention of health professionals in rural areas allows the development and enhancement of appropriate workforce strategies. Utilising the ‘rural pipeline’ as a template for medicine, nursing and allied health would strengthen current approaches to the recruitment and retention of professionals in rural areas and provide a consolidated evidence base.
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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 | 0.082 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".