Lifelong learning in active ageing discourse: its conserving effect on wellbeing, health and vulnerability
Bibliographic record
Abstract
ABSTRACT The Active Ageing Framework has been adapted as a global strategy in ageing policies, practices and research over the last decade. Lifelong learning, however, has not been fully integrated into this discourse. Using survey data provided by 416 adults (aged 60 years and above) enrolled in non-formal general-interest courses in a public continuing education programme in Canada, this study examined the association between older adults’ duration of participation in the courses and their level of psychological wellbeing, while taking their age, gender, self-rated health and vulnerability level into consideration. An analytical framework was developed based on the literature of old-age vulnerabilities and the benefits of lifelong learning. Two logistic regression and trend analyses were conducted. The results indicate that older adults’ participation is independently and positively associated with their psychological wellbeing, even among those typically classified as ‘vulnerable’. This result provides additional evidence that suggests the continuous participation in non-formal lifelong learning may help sustain older adults’ psychological wellbeing. It provides older learners, even those who are most vulnerable, with a compensatory strategy to strengthen their reserve capacities, allowing them to be autonomous and fulfilled in their everyday life. The result of this study highlights the value of the strategic and unequivocal promotion of community-based non-formal lifelong learning opportunities for developing inclusive, equitable and caring active ageing societies.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| 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".