Hospitals in rural or remote areas: An exploratory review of policies in 8 high-income countries
Bibliographic record
Abstract
Our study reviewed policies in 8 high-income countries (Australia, Canada, United States, Italy, Spain, United Kingdom, Croatia and Estonia) in Europe, Australasia and North America with regard to hospitals in rural or remote areas. We explored whether any specific policies on hospitals in rural or remote areas are in place, and, if not, how countries made sure that the population in remote or rural areas has access to acute inpatient services. We found that only one of the eight countries (Italy) had drawn up a national policy on hospitals in rural or remote areas. In the United States, although there is no singular comprehensive national plan or vision, federal levers have been used to promote access in rural or remote areas and provide context for state and local policy decisions. In Australia and Canada, intermittent policies have been developed at the sub-national level of states and provinces respectively. In those countries where access to hospital services in rural or remote areas is a concern, common challenges can be identified, including the financial sustainability of services, the importance of medical education and telemedicine and the provision of quick transport to more specialized services.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".