Digital Health Literacy in Adults With Low Reading and Writing Skills Living in Germany: Mixed Methods Study
Notice bibliographique
Résumé
Background: Digital health literacy is a key factor in enabling users to navigate in an increasingly digitalized health care system. Low levels of digital health literacy are associated with higher age, low education, and income, as well as low functional health literacy. Around 6.2 million adults living in Germany have low reading and writing skills. Due to their low literacy, this group is often underrepresented in research studies and therefore little is known about their digital health literacy and use of digital health tools. Objective: The objectives of this study were to assess digital health literacy in adults with low reading and writing skills and to explore which digital health tools they use in daily life. Methods: An interviewer-administered survey and focus groups were conducted with adult residents of Bremen, Germany, who were aged 18-64 years and had low reading and writing skills. In addition, a stakeholder workshop was held to derive recommendations on how digital health literacy could be improved. The survey questionnaire included 21 items addressing the use of digital health technologies and digital health literacy (eHealth Literacy Scale). Focus group participants completed several tasks on web-based health information and then discussed their experiences. Survey data were analyzed using descriptive statistics and linear regression. Qualitative content analysis was applied to analyze the focus group data and the written documentation of the stakeholder workshop. Results: Survey participants (n=96) were on average 43 (SD 10.7) years old, 72% (69/96) were female, and 92% (88/96) were not born in Germany. Participants reported mainly using information-related digital health technologies such as health apps (40/96, 42%), health websites (30/96, 31%), or activity trackers (27/96, 28%). The mean digital health literacy score was 22 (SD 8) points, with 35% (34/96) of participants classified as having a low digital health literacy (score between 8-19/40 points). Digital health technology use was associated with higher digital health literacy. For participants in the 5 focus groups (total n=39; mean age 43, SD 12.6 years; n=34, 87% female), limited technical skills and language problems were the most important challenges. Furthermore, focus group participants reported that they favor videos when searching for web-based health information and prefer to seek support from family members or local organizations for health issues. Stakeholders (n=15) recommended that health websites should be available in multiple languages, contain simple and easy-to-read language, and use images, symbols, and videos. Conclusions: While adults with low reading and writing skills use digital health technologies, many find it challenging to search for health information on the internet due to lacking technical skills and language problems. To ensure that adults with low reading and writing skills are not further left behind, future research should focus on developing tailored interventions to promote digital health literacy.
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 | 0,004 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| 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 ».