Intergenerational Aid in the 21st Century
Notice bibliographique
Résumé
As digital immigrants of the 21st century, the current elderly always seem to have difficulty catching up with today's technologies. When day-to-day services like banking, healthcare, travel, etc., become entirely digitized without giving seniors the required time, education, or support to get on board, it gradually chips at their independence, dignity, and agency. To keep afloat in this rapidly digitizing world, most seniors find themselves relying on assistance from the people around them, such as younger family members, neighbors, friends, and community volunteers. This research explores the various facets and multitudes of digital support that younger persons commonly provide seniors. What factors influence this intergenerational digital support between seniors and younger generations? What is the role of technology and its design in this context? Through qualitative interviews and participatory workshops, this thesis delves into the perspectives and lived experiences of various stakeholders like seniors, younger generations, community volunteers, tech coaches, etc. The research is also fundamentally informed by my experience as a regular volunteer at the West End Seniors' Network, an NGO offering social and community support for seniors in Vancouver. After a thorough thematic analysis of the data gathered, the paper derives key insights about intergenerational digital support under the following themes - (1) A Generational Divide, (2) The 'Why,' (3) The 'How,' (4) Benefits, and (5) Barriers. With these insights, the research attempts to situate the role of intergenerational aid in the broader picture of digital inclusivity for seniors. Intergenerational support is only a facet of this wicked problem; other stakeholders like family, community, government, private companies, etc., also share responsibility in keeping seniors apace with the digital world. This research is then applied to cohesively map out potential best practices for multiple stakeholders to improve digital literacy for seniors. However, while this is a more significant systemic change proposed for the long run, we could now take small steps and solutions to contribute towards the larger goal, like capitalizing on the benefits of this already widespread intergenerational digital support. In light of this, a mobile application is designed and prototyped to facilitate digital aid between seniors and younger persons with ease, efficiency, and warmth.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 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 ».