Active Aging From Theory to Practice: National Experiences of Policy Making in Europe and Canada
Bibliographic record
Abstract
Abstract Born in Europe as a concept aiming to counteract new demographic and societal challenges, active aging has progressively become a key pillar of an extended welfare state for aging populations in many high-income countries. Needs, interests, and preferences of new aging cohorts are changing, becoming more diverse and requiring a better understanding and greater attention by policy makers, beyond mere social welfare programmes for those with social, economic or health needs. Active aging policies aim at improving individuals’ quality of life by optimizing opportunities for health, participation, and security (WHO 2002), hence unlocking the potential of older people as active citizens in the community and the society. Since the focus is on a multidimensional concept of quality of life, active aging works at the intersection of labour, social, educational, family, infrastructure, and many other policy areas. However, there may be gaps and discrepancies between the concept in itself and its application at the policy level. The purpose of this symposium is to present and discuss how different post-industrial societies are advancing and implementing active aging policies, in the context of overarching societal challenges and competing needs. In this respect, the symposium focuses on four countries representing different traditional welfare state models: Canada, Italy, Poland, and the United Kingdom. These four case studies bring analyses of active aging policies at national and/or regional level, providing a picture of how such policies have been designed, how they evolved and what they have achieved in recent years.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.053 | 0.021 |
| Scholarly communication | 0.020 | 0.004 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".