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.
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.
How this classification was reachedexpand
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 | 0.008 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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; both teacher heads agree on what is shown here.
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".