MétaCan
Menu
Retour à la cohorte
Enregistrement W2801328592 · doi:10.2196/resprot.7764

Perceptions About Disseminating Health Information Among Mommy Bloggers: Quantitative Study

2018· article· en· W2801328592 sur OpenAlexvenueno aff
Gary L. Kreps, Kevin B. Wright

Notice bibliographique

RevueJMIR Research Protocols · 2018
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueSocial Media in Health Education
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésSocial mediaHealth communicationInformation DisseminationDisseminationPsychologyPerceptionPublic relationsMedical educationSocial psychologyInternet privacyMedicineWorld Wide WebPolitical scienceComputer science

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Social media are potentially powerful channels for communicating relevant health information in culturally sensitive and influential ways to key audiences. Moreover, these channels hold promise for promoting awareness and knowledge of health risks, prevention, and treatment by utilizing opinion leaders for message dissemination. Despite limited empirical evidence to-date, early promising results suggest that blogs are a form of social media that should be examined as worthy channels for health communication. OBJECTIVES: This formative study explored mommy bloggers' perceptions about sharing health-related information on their blogs with their readers. It also sought to analyze which topics would be of most interest to mommy bloggers, what motivates them to write about health issues, and how they perceive interest in these topics among their readers. METHODS: This study employed survey methodology, including the use of open-ended questions, the responses to which were coded for analysis. Specifically, a 14-item survey was fielded with mommy bloggers between October 1 and October 28, 2016. Bloggers were recruited through The Motherhood network. A total of 461 mommy bloggers responded to the survey; 163 were removed for low quality responses and incomplete data. As a result, 298 eligible participants completed the survey. For open-ended questions in the survey, a sample of responses were coded and analyzed. RESULTS: The majority of the respondents (87.2%, 260/298) reported that they have written about health issues in the past; 97.3% (290/298) of the respondents reported that they would consider writing about health issues sometime in the future, and 96.3% (287/298) of the respondents reported that their readers like to read about health issues on their blogs. In terms of content priorities for this sample of bloggers, Nutrition and Physical Activity dominate the current conversation and similarly, Physical Activity and Nutrition remain top content priorities for these bloggers for the future. Moreover, 21.3% of the respondents reported that their readers would be interested in these topics. Finally, having a personal connection with a health issue was found to be positively associated with likeliness to write about health issues on their blog (P<.001). CONCLUSIONS: This study illustrates that there are potentially rich opportunities for working with mommy bloggers to communicate with key health decision makers (moms) on important health issues. There is a great support among mommy bloggers for health information dissemination as well as interest for accessing relevant health information from their readers. This presents an opportunity for public health research and communication campaigns to more broadly promote their messages, thereby contributing to their behavior change objectives. Limitations included overrepresentation of white, higher-educated, and younger women. It suggests a need for more targeted engagement of a diverse sample for future work.

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 distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,010
score de la tête « metaresearch » (Gemma)0,010
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche, Études des sciences et des technologies
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Protocole · Signal consensuel: Protocole
Score de désaccord entre enseignants0,364
Score d'incertitude au seuil0,999

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0100,010
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,002
Études des sciences et des technologies0,0040,001
Communication savante0,0000,002
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,001

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.

Tête enseignante Opus0,549
Tête enseignante GPT0,702
Écart entre enseignants0,153 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeQualitatif
Domainenon disponible
GenreProtocole

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations37
Publié2018
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueJMIR Research ProtocolsMême sujetSocial Media in Health EducationTravaux en français237 207