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Record W4381386151 · doi:10.1017/s1049023x23001061

WHO Guidance on Research Methods for Health Emergency and Disaster Risk Management

2023· article· en· W4381386151 on OpenAlexaff
Ryoma Kayona, Mike Clarke, Virginia Murray, Emily Ying Yang Chan, Jonathan Abrahams, Tracey O’Sullivan

Bibliographic record

VenuePrehospital and Disaster Medicine · 2023
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsEmergency managementPreparednessContext (archaeology)Health policyHealth carePublic relationsMedicinePolitical sciencePublic healthNursing

Abstract

fetched live from OpenAlex

Introduction: The World Health Organization (WHO) has developed and supported numerous initiatives to build capacity and awareness about health emergency and disaster risk management (Health EDRM). These include establishing the Health EDRM Research Network (Health EDRM RN) in 2018 and the publication of the Health EDRM Framework in 2019. These initiatives recognize that research is vital to generating the evidence to inform decision making and research that is integral to disaster preparedness, response and recovery will be vital to delivering the aspirations associated with caring, coping and overcoming in an increasingly challenging world. Method: To strengthen the capacity for conduct and use of research, resources were developed by the WHO Guidance on Research Methods for Health EDRM. Results: This first WHO textbook on Health EDRM research methods was published in 2021 and updated in 2022 with a chapter on Health EDRM research in the context of COVID-19. The 44 chapters offer practical advice about how to plan, conduct and report on a variety of quantitative and qualitative studies that can inform questions about policies and programs for health-related emergencies and disasters across different settings and level of resources. Case studies of direct relevance to Health EDRM provide real-life examples of research methods and how they have modified policies. More than 160 authors in 30 countries contributed to the guidance, which is relevant to researchers, would-be researchers, policy makers and practitioners. It should help improve the quality of Health EDRM research; the quality of policy, practice and guidance supported by the evidence generated; and research capacity, collaboration and engagement among researchers, the research community, policy-makers, practitioners and other stakeholders. Conclusion: The Guidance is being supplemented by additional resources, including audio podcasts, slideshows, video presentations and webinars, and the content as a whole will be discussed in this presentation.

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)0.174
metaresearch head score (Gemma)0.231
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.826
Threshold uncertainty score0.921

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1740.231
Meta-epidemiology (narrow)0.0030.004
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0110.011
Science and technology studies0.0030.008
Scholarly communication0.0090.009
Open science0.0060.008
Research integrity0.0090.015
Insufficient payload (model declined to judge)0.0530.040

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 head0.168
GPT teacher head0.583
Teacher spread0.415 · 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
DomainMethods
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

Citations33
Published2023
Admission routes1
Has abstractyes

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