Rural emergency departments: A systematic review to develop a resource typology relevant to developed countries
Bibliographic record
Abstract
OBJECTIVE: Despite low patient numbers, rural emergency departments have a similar diversity of case presentations as urban tertiary hospitals, with the need to manage high-acuity cases with limited resources. There are no consistent descriptions of the resources available to rural emergency departments internationally, limiting the capacity to compare clinical protocols and standards of care across similarly resourced units. This review aimed to describe the range of human, physical and specialist resources described in rural emergency departments in developed countries and propose a typology for use internationally. DESIGN AND SETTING: A systematic literature search was performed for journal articles between 2000 and 2019 describing the staffing, access to radiology and laboratory investigations, and hospital inpatient specialists. RESULTS: Considerable diversity in defining rurality and in resource access was found within and between Australia, New Zealand, Canada and USA. DISCUSSION: A typology was developed to account for (a) emergency department staff on-floor, (b) emergency department staff on-call, (c) physical resources and (d) access to a specialist surgical service. This provides a valuable tool for relevant stakeholders to effectively communicate rural emergency department resources within a country and internationally. CONCLUSION: The proposed five-tiered typology draws together international literature regarding rural emergency department services. Although further research is required to test this tool, the formation of this common language allows a base for effective communication between governments, training providers and policy-makers who are seeking to improve health systems and health outcomes.
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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 | 0.013 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".