The role of national policies to address rural allied health, nursing and dentistry workforce maldistribution
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
Objective: Maldistribution of the health workforce between rural, remote and metropolitan communities contributes to longstanding health inequalities. Many developed countries have implemented policies to encourage health care professionals to work in rural and remote communities. This scoping review is an international synthesis of those policies, examining their effectiveness at recruiting and retaining nursing, dental and allied health professionals in rural communities. Study design: Using scoping review methods, we included primary research - published between 1 September 2009 and 30 June 2020 - that reported an evaluation of existing policy initiatives to address workforce maldistribution in high income countries with a land mass greater than 100 000 km(2). Data sources: We searched MEDLINE, Ovid Embase, Ovid Emcare, Informit, Scopus, and Web of Science. We screened 5169 articles for inclusion by title and abstract, of which we included 297 for full text screening. We then extracted data on 51 studies that had been conducted in Australia, the United States, Canada, United Kingdom and Norway. Data synthesis: We grouped the studies based on World Health Organization recommendations on recruitment and retention of health care workers: education strategies (n = 27), regulatory change (n = 11), financial incentives (n = 6), personal and professional support (n = 4), and approaches with multiple components (n = 3). Conclusion: Considerable work has occurred to address workforce maldistribution at a local level, underpinned by good practice guidelines, but rarely at scale or with explicit links to coherent overarching policy. To achieve policy aspirations, multiple synergistic evidence-based initiatives are needed, and implementation must be accompanied by well designed longitudinal evaluations that assess the effectiveness of policy objectives.
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 | 0.001 | 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 | 0.004 | 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