The Influencing Factors and Process of Becoming and Remaining an \nAge-Friendly University
Notice bibliographique
Résumé
The Influencing Factors and Processes of Becoming and Remaining an Age-Friendly University Population ageing and urbanisation are twin global trends shaping the 21st century. The rise of older populations in expanding cities underscores their value as assets for families, \ncommunities, and economies in fostering supportive living environments. The World Health Organization (WHO) defines active ageing as a lifelong process influenced by various factors that promote health, participation, and security in older adult life. In 2006, the WHO initiated the Age-Friendly Cities Programme, delineating eight domains to foster \nhealthy and active ageing as both the physical and social environments within our cities and communities significantly shape the experiences and opportunities of older people. Universities contribute to fostering an age-friendly society through their roles in education, research, wellness initiatives, and providing cultural and social opportunities.In 2012, Dublin City University (DCU) launched the Ten Principles of an Age-Friendly \nUniversity. This initiative influenced the development of a global network of over 100 higher education institutions committed to implementing these principles. A substantial and expanding body of literature delineates age-friendliness across various domains such as cities, businesses, housing, healthcare, transportation, and communities, fostering \ncollaborative efforts to define best practices. However, the concept of an Age-Friendly University is relatively new. Scant literature exists on the process of AFU members towards joining the global network or the factors influencing their decisions. The interpretation and implementation of AFU principles vary globally, warranting research due to the network's rapid growth. This study addresses this gap by investigating the \ninfluencing factors and processes involved in becoming and maintaining AFU status. It will delve into the decision-making considerations, analyse the interpretation and implementation of the Ten Principles, and identify their broader impact on higher education. The pioneering study employed a mixed-methods approach, integrating a quantitative survey, two case studies (McMaster University, Canada and the University of Masaryk, \nCzech Republic), and document analysis. Key findings reveal that prioritised principles such as intergenerational learning, promoting longevity dividends, and integrating older people into core university activities are of prime importance to members of the AgeFriendly University Global Network and are influenced by critical factors including \nsocietal needs, fostering age inclusivity, and promoting intergenerational engagement.
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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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,008 | 0,002 |
| Communication savante | 0,002 | 0,006 |
| Science ouverte | 0,002 | 0,001 |
| 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 ».