EMS prehospital response to the COVID-19 pandemic in the US: A brief literature review
Bibliographic record
Abstract
ABSTRACT This study aimed to analyze prehospital Emergency Medical Services (EMS) response to the COVID-19 pandemic in the US through a brief systematic review of available literature in context with international prehospital counterparts. An exploration of the NCBI repository was performed using a search string of relevant keywords which returned n=5128 results; articles that met the inclusion criteria (n=77) were reviewed and analyzed in accordance with PRISMA and PROSPERO recommendations. Methodical quality was assessed using critical appraisal tools, and the Egger’s test was used for risk of bias reduction upon linear regression analysis of a funnel plot. Sources of heterogeneity as defined by P < 0.10 or I^2 > 50% were interrogated. Findings were considered within ten domains: structural/systemic; clinical outcomes; clinical assessment; treatment; special populations; dispatch/activation; education; mental health; perspectives/experiences; and transport. Findings suggest, EMS clinicians have likely made significant and unmeasured contributions to care during the pandemic via nontraditional roles, i.e., COVID-19 testing and vaccine deployment. EMS plays a critical role in counteracting the COVID-19 pandemic in addition to the worsening opioid epidemic, both of which disproportionately impact patients of color. As such, being uniquely influential on clinical outcomes, these providers may benefit from standardized education on care and access disparities such as racial identity. Access to distance learning continuing education opportunities may increase rates of provider recertification. Additionally, there is a high prevalence of vaccine hesitancy among surveyed nationally registered EMS providers. Continued rigorous investigation on the impact of COVID-19 on EMS systems and personnel is warranted to ensure informed preparation for future pandemic and infectious disease response.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.020 | 0.016 |
| 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) | 0.005 | 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".