Inclusive Design: To AgeTech or not to AgeTech?
Notice bibliographique
Résumé
The United Nations has identified population ageing as a global phenomenon, with virtually every country in the world experiencing growth in the size and proportion of older persons in their populations. Specifically, the share of the population 65 plus will increase from 9% in 2019 to 16% by 2050, more than doubling from 703 million to 1.5 billion. In this context, AgeTech is expected to be a $2.7 trillion global industry by 2025, based upon having a 10% share of the growing global Longevity Economy. So, companies and investors are understandably interested in technology that would bring living longer closer to living well. While the promise of such technology is preferable, the approach of AgeTech to be exclusively designed for older people is problematic; as lacking Inclusive Design in the initial development of digital technologies cannot be remedied by further lacking Inclusive Design in subsequent specialist AgeTech. Such specialist products and services would be inherently limited, even assuming gender and ethnic inclusivity. They would likely be crisis purchases bought because of need rather than desire, lacking appeal because of potential or perceived stigma. This is because such ageism can significantly affect how ageing is understood in design, for example the notion of 'senior' can be associated with illness and/or disability. However, it can be estimated, at least for developed countries, that the majority of seniors are fully physically and mentally able. For example, in the United Kingdom, from their Office for National Statistics data, 58% of those at or above state pension age (i.e. senior) are fully physically and mentally able; and for Canada, from their Statistics Canada data, the proportion is similarly estimated to be 62%. So, designers and developers need to move beyond ageist stereotypes as ageing populations are diverse, requiring design to understand and embody their diversity. Therefore, we consider moving beyond ageist stereotypes, negative and positive, in designing preferable technology futures of living well longer. Positive stereotypes can also be harmful, for example sageism, in which the notion of 'elder' can create expectations on older people that cannot subsequently be met. Overall, adopting Inclusive Design in the development of digital technologies would ensure usefulness and appeal to adults of all ages, for inclusive rather than ageist technology.
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 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 | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,002 | 0,003 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,003 | 0,005 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».