The economic impact of rural healthcare on rural economies: A rapid review
Bibliographic record
Abstract
Introduction: One critical component of any rural community is its healthcare system. Rural healthcare systems are essential as rural communities have worse health outcomes when compared to urban areas. Rural healthcare systems might also have a positive impact on rural economies. In some rural areas, these health services are threatened with a reduction or closure. This rapid review was carried out to examine the impact of rural healthcare systems' declines on rural economies. Methods: We conducted a rapid review of peer-reviewed and grey literature sources on studies that examined the economic impact of rural healthcare on rural economies in Canada, Australia, Scandinavia and the United States of America (USA). We used a data extraction template adapted from the Centre for Reviews and Dissemination. Results: We found 17 research papers between two databases and nine websites. Articles examined various health professions (dentist, physician assistant and pharmacist), the inclusion of family physicians, a physician with an increased scope of practice (obstetrics and surgery), the impact of a rural primary care hospital, telemedicine, a distributed medical education programme and the health care sector. Conclusion: Rural healthcare seems to have a positive impact on jobs and labour-based wages in rural communities. There is a considerable need for research outside the USA.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".