Applying Website Rankings to Digital Health Centers in the United States to Assess Public Engagement: Website Usability Study
Notice bibliographique
Résumé
BACKGROUND: As the public increasingly uses the internet to search for resources and information regarding health and medicine, it is important that health care organizations provide adequate web resources. Website usability refers to the ease of user experience on a website. In this study, we conducted usability analyses on digital health center websites. OBJECTIVE: The primary aims of this study were to (1) replicate a preexisting usability scoring methodology for digital health centers; (2) apply and test this replicated usability scoring methodology on a sample set of digital health center websites; and (3) derive recommendations from the results on potential areas of improvements for our sample of digital health center websites. METHODS: Website usability testing was conducted from March 1, 2020, to March 15, 2020. We replicated a methodology and scoring system from previous literature and applied them to digital health center websites. Our sample included 67 digital health centers that were affiliated with US universities or hospital systems. Usability was split into the following four broad categories: accessibility, marketing, content quality, and technology. Usability tools were used to score websites in each of the four categories. The composite of the key factors of each category was used to generate a general usability and overall usability score for each website. RESULTS: The category with the highest average score (6.3) was content quality. The content quality score also had the highest SD (2.18) and an SE of 0.27. The lowest performing category was technology, which had an average score of 0.9. The technology score also had the smallest SD (0.07) and an SE of 0.01. CONCLUSIONS: Our data suggest that content quality, on average, was the highest scoring variable among digital health center websites. As content is crucial to digital health knowledge, it is justified that digital health centers invest more resources into creating quality content. The overall lowest scoring variable was technology. Potential reasons for this finding include designated funding for servers, a lack of regulatory frameworks for social media presence and liability, and infrequent website audits. An easy approach for improving this variable is increasing website speed. Accessibility is another area that organizations can potentially improve. We recommend that these organizations perform periodic audits of their web presence with usability tools.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 tête enseignante, 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 ».