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Record W2122499596 · doi:10.1108/dpm-11-2014-0224

Developing an evaluation tool for disaster risk messages

2015· article· en· W2122499596 on OpenAlexaboutno aff
Caroline D. Bergeron, Daniela B. Friedman

Bibliographic record

VenueDisaster Prevention and Management An International Journal · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Relations and Crisis Communication
Canadian institutionsnot available
Fundersnot available
KeywordsEmergency managementAgency (philosophy)PreparednessRisk communicationComprehensionFocus groupPublic healthRisk managementKnowledge managementRisk analysis (engineering)Computer scienceBusinessMedicineNursingPolitical science

Abstract

fetched live from OpenAlex

Purpose – Risk communication is a critical component of individual health decision making and behavior. In disaster situations, it is crucial that risk-related messages are communicated accurately and that they reach and inform target audiences about the steps they can take to protect their health. Despite a global recognition of the importance of risk communication in responding to disasters, there remains a dearth of evidence on how to evaluate the effectiveness of risk communication messages. The purpose of this paper is to develop and assess a pilot tool to evaluate the effectiveness of disaster risk messages. Design/methodology/approach – A pilot evaluation tool was developed using the existing risk communication literature. An expert assessment of the tool was conducted using an open-ended survey and a focus group discussion with 18 experts at the Public Health Agency of Canada in February 2013. Findings – The tool measures content, reach, and comprehension of the message. It is intended to be a quick, internal evaluation tool for use during a disaster or emergency. The experts acknowledged the practicality of the tool, while also recognizing evaluation challenges. Research limitations/implications – This pilot exploratory tool was assessed using a relatively small sample of experts. Practical implications – This tool offers public health and disaster preparedness practitioners a promising approach for evaluating and improving the communication and management of future public health emergencies. Originality/value – This is the first practical tool developed to evaluate risk communication messages in disaster situations.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.599
Threshold uncertainty score0.780

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.117
GPT teacher head0.440
Teacher spread0.323 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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

Citations15
Published2015
Admission routes1
Has abstractyes

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