Development and Implementation of the World Health Organization Emergency Medical Teams: Minimum Technical Standards and Recommendations for Rehabilitation
Bibliographic record
Abstract
Emergency medical teams provide urgent medical and surgical care in emergencies characterized by a surge in trauma or disease. Rehabilitation has historically not been included in the acute phase of care, as teams have either not perceived it as their responsibility or have relied on external providers, including local services and international organizations, to provide services. Low- and middle-income countries, which often have limited rehabilitation capacity within their health system, are particularly vulnerable to disaster and are usually ill-equipped to address the increased burden of rehabilitation needs that arise. The resulting unmet needs for rehabilitation culminate in unnecessary complications for patients, delayed recovery, reduced functional outcomes, and often impede return to daily activities and life roles. Recognizing the systemic neglect of rehabilitation in global emergency medical response, the World Health Organization, in collaboration with key operational partners and experts, developed technical standards and recommendations for rehabilitation which are integrated into the WHO verification process for EMTs. This protocol report presents: 1) the rationale for the development of the standards and accompanying recommendations; 2) the methodology of the development process; 3) the minimum standards and other significant content included in the document; 4) challenges encountered during development and implementation; and 5) current and next steps to continue strengthening the inclusion of rehabilitation in emergency medical 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 | 0.243 | 0.315 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.008 | 0.008 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".