WHO’ Minimum Technical Standards and Recommendations for Rehabilitation, for Emergency Medical Teams’ Guidance: Development and Use
Bibliographic record
Abstract
Study/Objective: This field case study highlights how Emergency Medical Teams (EMTs) have bridged the gap between the emergency response and early recovery, of a health-system affected by a Sudden Onset Disaster (SOD), by supporting continuity-of-care and capacity building, using the experience and methodology of the Canadian Red Cross (CRC).Background: Following a SOD, many EMTs leave the affected country at the two-week mark.This can lead to significant gaps in the continuity of health service delivery, resulting in heightened public health risks within a fragile health-system.The CRC has been deploying Emergency Field Hospitals (EFHs) in response to disasters since 1996.Recent external evaluations of CRC deployments (type 1 or 2) deployed to the Philippines, Nepal and Ecuador, have validated the success of the approach of CRC.CRC has handed over the EFH in the Philippines, Nepal and Ecuador.Methods: CRC partners with the Red Cross of the affected country when deploying an EMT.Contrary to many EMTs, these deployments typically range from 1-4 months, and are followed by additional programming.CRC's handover process includes training and donation of medical equipment to a local partner.A desk review, analysis of operational data, and expert interviews have identified the vital role of EMTs in recovery and health-system strengthening, by staying longer than two weeks, delivering more than clinical services, employing a comprehensive handover, and embedding services within the health-system.Results: The Ministry of Health in the Philippines, Nepal and Ecuador were able to ensure service-delivery, despite the departure of CRC and health-systems that were not fully rehabilitated.Additionally, the Philippine Red Cross has deployed its own newly-acquired EFH, to more than 5 operations.Similar results can be seen in Nepal and Ecuador.Conclusion: Evidence will inform organizations deploying EMTs methods to improve continuity-of-care, and the critical role of EMTs in accelerating disaster-recovery.
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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 | 0.114 | 0.195 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.007 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.011 | 0.010 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.011 | 0.007 |
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".