Learning in the UN Decade of Healthy Ageing: insights from Canada and the UK
Bibliographic record
Abstract
This article explores how learning contributes to healthy ageing, and it reinforces learning as an important aspect of the sustainable ageing process. It begins with an analysis of the framework established by the UN Decade of Healthy Ageing, pinpointing the notable absence of learning as a crucial element of well-being in the later stages of life. Then, it explores the research in educational gerontology, specifically focusing on how learning has been conceptualised as a lifelong endeavour. Subsequently, the article presents three illustrative cases: a language learning programme designed for older immigrants in Canada, an intergenerational somatic co-creation workshop in the UK, and a UK public educational campaign tailored to the multifaceted needs of ageing populations. The article places the three multidisciplinary learning initiatives against the backdrop of the United Nations Decade of Healthy Ageing. The discussion reveals how each case contributes to the redefinition of healthy ageing, increased knowledge of age-related processes, older peoples’ confidence in self-management and a wider range of lifestyle choices for healthy ageing. By integrating the concept of well-being into thinking about learning in later life, the article showcases how the inclusive, participatory approach and co-production of tailored learning initiatives can powerfully contribute to healthy ageing across diverse groups. To conclude, the authors argue for expanding policy frameworks to include different learning contexts that reflect the diverse needs and experiences of older adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".