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Enregistrement W4379659640 · doi:10.24908/agt.v1i1.16397

Older adults in a Modern World

2023· article· en· W4379659640 sur OpenAlexaffabout
L.F. Carver

Notice bibliographique

RevueAging and (Geron) Technology · 2023
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueTechnology Use by Older Adults
Établissements canadiensQueen's University
Organismes subventionnairesnon disponible
Mots-clésLiberian dollarGovernment (linguistics)Investment (military)PhoneFeelingGerontologyPsychologyBusinessPolitical scienceMedicinePublic relationsFinancePoliticsSocial psychology

Résumé

récupéré en direct d'OpenAlex

Editorial Older adults in a Modern WorldBy L.F. Carver, Editor-in-Chief Stereotypes that suggest older adults are resistant to technology may have had some accuracy in the 1980s but are now outdated and inaccurate. Technology has been evolving quickly over the past century, and older adults have been adapting to these changes just as quickly. Telephones went from a single wall mounted phone in the home to cordless handsets and cellphones, changes driven by the buying habits of people who are now older adults. The consumers who enthusiastically embraced home computers, then laptops and finally tablets are now middle aged or older adults.(Geron)technologies are those technologies that support aging adults – but these do not need to be limited to health-related devices. Older adults generally report feeling much younger inside than their chronological age and buy technology that supports them to engage in hobbies and activities that they did in their twenties and thirties. These technologies often mirror those chosen by their younger counterparts.Governments are recognizing the importance of AgeTech with investments in the development of these industries. For example, in late 2022/early 2023 the Canadian government announced a $47 million dollar investment (via the Strategic Innovation Fund) and the American National Institute on Aging (a2 Pilot Awards) unveiled $40 million for AgeTech over five years. Given the inaccuracies of traditional ageist stereotypes it is important to avoid surveillance and communal residential settings (e.g., long term care homes) as the focus for AgeTech, especially given most older adults are uninterested in living these facilities and technology that violates their privacy.Modern older adults are interested in technologies that enable them maintain independence, aging gracefully in place, in their own homes. However, they tend to reject technologies that impinge on privacy – especially those that involve 24/7 surveillance. Ambient assisted living, which generally involves cameras and pressure plates that record, and report, movement and activity are a favourite among caregivers and concerned family but are generally repudiated by older adults.Finally, older adults who refuse certain technologies are not ‘resistant’ to tech, they just don’t want technology to try to fool them or replace reality. If they enjoy travel, they want technology that allows them to continue to travel as long as possible, not virtual reality goggles. Older animal lovers don’t want a robot pet, they want technology to support them to continue to care for their non-human companions as long as possible.The most important thing for policy makers, technology companies and younger adults to remember is that older adults share many of the same consumer interests when it comes to technology as younger adults. In fact, instead of considering older adults as something different or ‘other’ than their younger counterparts, we need to remember that they are the same people as they were when they were younger, they just have more mileage.

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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,697
Score d'incertitude au seuil0,990

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,003
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,012
Tête enseignante GPT0,280
Écart entre enseignants0,268 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeAutre devis
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission2
Résumé présentoui

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