Older Adults’ Experiences With Using Technology for Socialization During the COVID-19 Pandemic: Cross-sectional Survey Study
Notice bibliographique
Résumé
BACKGROUND: Technology use has become the most critical approach to maintaining social connectedness during the COVID-19 pandemic. Older adults (aged >65 years) are perceived as the most physiologically susceptible population to developing COVID-19 and are at risk of secondary mental health challenges related to the social isolation that has been imposed by virus containment strategies. To mitigate concerns regarding sampling bias, we analyzed a random sample of older adults to understand the uptake and acceptance of technologies that support socialization during the pandemic. OBJECTIVE: We aimed to conduct a population-based assessment of the barriers and facilitators to engaging in the use of technology for web-based socialization among older adults in the Canadian province of British Columbia during the COVID-19 pandemic. METHODS: We conducted a cross-sectional, population-based, regionally representative survey by using the random-digit dialing method to reach participants aged >65 years who live in British Columbia. Data were analyzed using SPSS (IBM Corporation), and open-text responses were analyzed via thematic analysis. RESULTS: Respondents included 400 older adults aged an average of 72 years, and 63.7% (n=255) of respondents were female. Most respondents (n=358, 89.5%) were aware of how to use technology to connect with others, and slightly more than half of the respondents (n=224, 56%) reported that, since the beginning of the pandemic, they used technology differently to connect with others during the pandemic. Additionally, 55.9% (n=223) of respondents reported that they adopted new technology since the beginning of the pandemic. Older adults reported the following key barriers to using technology: (1) a lack of access (including finance-, knowledge-, and age-related issues); (2) a lack of interest (including a preference for telephones and a general lack of interest in computers); and (3) physical barriers (resultant of cognitive impairments, stroke, and arthritis). Older adults also reported the following facilitators: (1) a knowledge of technologies (from self-teaching or external courses); (2) reliance on others (family, friends, and general internet searches); (3) technology accessibility (including appropriate environments, user-friendly technology, and clear instructions); and (4) social motivation (everyone else is doing it). CONCLUSIONS: Much data on older adults' use of technology are limited by sampling biases, but this study, which used a random sampling method, demonstrated that older adults used technology to mitigate social isolation during the pandemic. Web-based socialization is the most promising method for mitigating potential mental health effects that are related to virus containment strategies. Providing telephone training; creating task lists; and implementing the facilitators described by participants, such as facilitated socialization activities, are important strategies for addressing barriers, and these strategies can be implemented during and beyond the pandemic to bolster the mental health needs of older adults.
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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 | 0,002 | 0,005 |
| 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 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 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 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 ».