MétaCan
Menu
Back to cohort

Development and Implementation of the World Health Organization Emergency Medical Teams: Minimum Technical Standards and Recommendations for Rehabilitation

2018· article· en· W2880900867 on OpenAlexaff
Jody-Anne Mills, James E. Gosney, Fiona Stephenson, Peter Skelton, Ian Norton, Valerie Scherrer, G. Jacquemin, B. Rau

Bibliographic record

VenuePLoS Currents · 2018
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsUniversité de MontréalInstitut de Readaptation Gingras Lindsay de Montreal
FundersWorld Health Organization
KeywordsRehabilitationMedicineMedical emergencyNeglectProtocol (science)Health careProcess (computing)Emergency medical servicesInclusion (mineral)Process managementNursingBusinessPhysical therapyPsychologyComputer scienceAlternative medicinePolitical science

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.243
metaresearch head score (Gemma)0.315
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.243
Threshold uncertainty score0.934

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2430.315
Meta-epidemiology (narrow)0.0020.003
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0080.004
Science and technology studies0.0040.005
Scholarly communication0.0100.007
Open science0.0080.008
Research integrity0.0080.011
Insufficient payload (model declined to judge)0.0070.006

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.

Opus teacher head0.050
GPT teacher head0.474
Teacher spread0.423 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreMethods

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".

Quick stats

Citations29
Published2018
Admission routes1
Has abstractyes

Explore more

Same venuePLoS CurrentsSame topicDisaster Response and ManagementFrench-language works237,207