Getting the Message Out: Social Media and Word-of-Mouth as Effective Communication Methods during Emergencies
Notice bibliographique
Résumé
Effective communication is a critical part of managing an emergency. During an emergency, the ways in which health agencies normally communicate warnings may not reach all of the intended audience. Not all communities are the same, and households within communities are diverse. Because different communities prefer different communication methods, community leaders and emergency planners need to know their communities' preferred methods for seeking information about an emergency. This descriptive report explores findings from previous community assessments that have collected information on communication preferences, including television (TV), social media, and word-of-mouth (WoM) delivery methods. Data were analyzed from 12 Community Assessments for Public Health Emergency Response (CASPERs) conducted from 2014-2017 that included questions regarding primary and trusted communication sources. A CASPER is a rapid needs assessment designed to gather household-based information from a community. In 75.0% of the CASPERs, households reported TV as their primary source of information for specific emergency events (range = 24.0%-83.1%). Households reporting social media as their primary source of information differed widely across CASPERs (3.2%-41.8%). In five of the CASPERs, nearly one-half of households reported WoM as their primary source of information. These CASPERs were conducted in response to a specific emergency (ie, chemical spill, harmful algal bloom, hurricane, and flood). The CASPERs conducted as part of a preparedness activity had lower percentages of households reporting WoM as their primary source of information (8.3%-10.4%). The findings in this report demonstrate the need for emergency plans to include hybrid communication models, combining traditional methods with newer technologies to reach the broadest audience. Although TV was the most commonly reported preferred source of information, segments of the population relied on social media and WoM messaging. By using multiple methods for risk communication, emergency planners are more likely to reach the whole community and engage vulnerable populations that might not have access to, trust in, or understanding of traditional news sources. Multiple communication channels that include user-generated content, such as social media and WoM, can increase the timeliness of messaging and provide community members with message confirmation from sources they trust encouraging them to take protective public health actions.WolkinAF, SchnallAH, NakataNK, EllisEM. Getting the message out: social media and word-of-mouth as effective communication methods during emergencies. Prehosp Disaster Med. 2019;34(1):89-94.
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Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,014 | 0,054 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,006 | 0,009 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».