The Influencing Factors and Process of Becoming and Remaining an \nAge-Friendly University
Bibliographic record
Abstract
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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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".