Comparison between two Canadian Provinces on technology use for social interaction by older adults: comparative cross-sectional survey study
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic prompted most people to embrace different approaches to their daily activities. Due to various measures to slow transmission in many jurisdictions, older adults were particularly impacted by measures restricting interactions. Our previous cross-sectional study identified barriers and facilitators for technology use for social interaction among older adults in British Columbia (BC), Canada. To investigate whether regional differences exist, the same survey from the previous study was conducted in Saskatchewan (SK), Canada during the same time. We also explored whether education and income levels were associated with older adults' social technology usage. METHODS: The cross-sectional survey was conducted through random-digit dialing to older adults who were 65 or older in BC and SK. Data were analyzed through the Statistical Package for the Social Sciences (SPSS) software (IBM corporation) and Microsoft Excel. Thematic analysis was performed on the survey's responses to open-ended questions. RESULTS: There were 806 participants, 400 from BC and 406 from SK. Education levels were associated with awareness of using technology for social interaction for both BC and SK while only SK had an association between new technology use and education levels. Similarly, income levels were also associated with awareness of technology use for social interaction for both provinces while only BC had an association between income levels to uptake of new technology. From the previous and current study, the barriers identified for technology use for social interactions in BC and SK were lack of interest, access (including financial issues) and physical limitations. SK participants identified perceived low self-efficacy as an additional barrier. For facilitators, BC and SK participants identified current and previous technology knowledge, help from others and motivation to keep social connections. Access to technology was unique to BC while better technology was unique to SK. CONCLUSIONS: Our study suggests that when older adults have access to resources to support their technology use, they will use them more, possibly enhancing their capacity for technology use. Future studies with more diverse populations around Canada may identify varying factors for older adults' technology use and regional variations in how technology is used for social interaction.
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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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».