Older Adults’ Participation in Education and Successful Aging: Implications for University Continuing Education in Canada
Bibliographic record
Abstract
Representatives from Manitoba seniors’ organizations and the University of Manitoba collaborated on a proposal to examine the participation of older adults in learning activities. The initiative led to a series of studies on this theme, including an exploration of participation at a seniors’ centre (Sloane-Seale & Kops, 2004), a comparison of participants and non-participants at three selected urban seniors’ centres (Sloane-Seale & Kops, 2007), and an analysis of participation at several urban and rural seniors’ centres, as well as participants’ perceptions of the characteristics of successful aging (Sloane-Seale & Kops, 2008). Building on these previous studies, the study described in this article examined the participation of older adults in Manitoba and how it links to successful aging. Key statistics relating to older adults’ participation, types of educational activities, learning in later life, and characteristics of successful aging were collected. The results suggest that such participation leads to a more inclusive and comprehensive understanding of successful aging; that educational activities positively influence mental and physical activity, which in turn result in more positive health and well- being; and that spirituality and life planning, including a positive sense of self, a focus on personal renewal and growth, a connection to the broader community, and setting life goals, contribute to successful aging. In light of Canada’s aging population, these findings have implications for educational gerontology, lifelong learning, and continuing education practice and research.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".