Assessing and Responding in Real Time to Online Anti-vaccine Sentiment during a Flu Pandemic
Notice bibliographique
Résumé
The perceived safety of vaccination is an important explanatory factor for vaccine uptake and, consequently, for rates of illness and death.The objectives of this study were (1) to evaluate Canadian attitudes around the safety of the H1N1 vaccine during the fall 2009 influenza pandemic and (2) to consider how public health communications can leverage the Internet to counteract, in real time, anti-vaccine sentiment.We surveyed a random sample of 175,257 Canadian web users from October 27 to November 19, 2009, about their perceptions of the safety of the HINI vaccine.In an independent analysis, we also assessed the popularity of online flu vaccine-related information using a tool developed for this purpose.A total of 27,382 unique online participants answered the survey (15.6% response rate).Of the respondents, 23.4% considered the vaccine safe, 41.4% thought it was unsafe and 35.2% reported ambivalence over its safety.Websites and blog posts with anti-vaccine sentiment remained popular during the course of the pandemic.Current public health communication and education strategies about the flu vaccine can be complemented by web analytics that identify, track and neutralize anti-vaccine sentiment on the Internet, thus increasing perceived vaccine safety.Counter-marketing strategies can be transparent and collaborative, engaging online "influencers" who spread misinformation.P rior to the 2009 influenza A (H1N1) pandemic, public health experts recognized that communities throughout the globe were deficient in pandemic planning (Mareinniss et al. 2009) and could benefit from strategies to increase vaccination rates.In any epidemic, high vaccination uptake is essential in order to limit transmission, protect groups at high risk, reduce the number of severe outcomes and prevent an overload of health services use.Inadequate information about the protective effects of a demonstrably safe flu vaccine reduces immunization rates, contributing to a more rapid spread and wider distribution of an epidemic.Healthcare workers are at particular risk, and, accordingly, in some jurisdictions such as Ontario, it is a hospital board-level responsibility to ensure rapid-response emergency preparedness plans are in place to protect the safety of hospital workers in the event of an infectious outbreak (Seeman et al. 2008).Systematic reviews show that vaccines prevent infection, complication and death, especially when provided to groups at high risk (Jefferson et al. 2008).Why, then, do many people choose not to be vaccinated?Reasons include a lack of familiarity with the epidemiological facts, a lack of support or notification from the healthcare system and unfounded fears about vaccine safety (Baeyens 2010;Maurer et al. 2010).Common fears are that a new vaccine has been rushed to production with insufficient prior research, that it has not been adequately tested and that long-term studies are needed (Seale et al. 2010).
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».