Review article: Impact of pandemics on rural emergency departments: A scoping review
Bibliographic record
Abstract
Pandemics can cause much distress to communities and present a major burden to the resources and functioning of hospitals. This scoping review aimed to identify, evaluate and summarise current literature regarding how pandemics impact rural EDs in terms of staff wellbeing, structure, function and resources. A systematic search of six databases using search terms including pandemic, ED and rural and remote was undertaken. Articles were included if they were peer-reviewed, written in English, original research, published between January 2010 and October 2021 and discussed the impact of pandemics on rural EDs. Articles were critically appraised using the Mixed Methods Appraisal Tool (MMAT). Three articles, one from Canada and two from the United States, met the inclusion criteria. The articles included were quantitative in design and fulfilled most of the MMAT critical analysis criteria. Pandemics reported on included H1N1 and COVID-19. These pandemics impacted rural EDs in terms of functioning and resourcing; no description of staff wellbeing or structure was identified. Rural ED functioning was affected in terms of input; with an increase in patient presentations and time to physician assessment during H1N1, but a decrease in patient presentations and transfers during COVID-19. Rural ED resources were impacted in regard to staffing, difficulty in obtaining stocks of personal protective equipment and medication, and community response. Further research to understand and address the short- and long-term impacts pandemics may have on rural EDs is required.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".