Developing an evaluation tool for disaster risk messages
Bibliographic record
Abstract
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.
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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 | 0.081 | 0.214 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".