Disaster Responder Competencies for Emergency Medical Teams: A Scoping Review
Bibliographic record
Abstract
Background/Introduction: Historically, medical response efforts to large-scale disaster events have highlighted significant variability in the capabilities of responding medical providers and emergency medical teams (EMTs). Analysis of the 2010 Haiti earthquake response found that a number of medical teams were poorly prepared, inexperienced, or lacked the competencies to provide the level of medical care required, highlighting the need for medical team standards. The World Health Organization (WHO) EMT initiative that followed created minimum team standards for responding international EMTs to improve the quality and timeliness of medical services. At the present time however, there remains a lack of globally recognized minimum competency standards at the level of the individual disaster medical responder, allowing for continued variability in patient care. Objectives: This study examines existing competencies for physicians, nurses, and paramedics who are members of deployable disaster response teams. Method/Description: A scoping review of published English-language articles on existing competencies for physicians, nurses, and paramedics who are members of deployable disaster response teams was performed in Ovid MEDLINE, Ovid Embase, CINAHL, Scopus, and Web of Science Core Collection. A total of 3,474 articles will be reviewed. Results/Outcomes: Data to be analyzed by October 1, 2024. Conclusion: There is a need to develop minimum standards for healthcare providers on disaster response teams. Identification of key existing competencies for disaster responders will provide the foundation for the creation of globally recognized minimum competency standards for individuals seeking to join an EMT in the future and will guide training and curricula development.
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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 | 0.021 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".