Later Life Learning Experience Among Chinese Elderly in Hong Kong
Bibliographic record
Abstract
In a world with increasing numbers of older adults and a world wide emphasis placed on lifelong learning, it is crucial to examine and formulate appropriate policy for learning in later life (LLL). Hong Kong has a rapidly aging population, which is projected to double within the next 25 years. However, lifelong learning for the elderly has yet to be fully developed. This article reports the findings of 2 surveys: one on the LLL experience among 190 Chinese elderly in Hong Kong and another on the experiences of 9 center directors in running courses for the elderly. We found that Chinese older persons generally learn for expressive motivation rather than instrumental motivation, although those with higher educational attainment take LLL for both instrumental and expressive motivation. This finding is consistent with those obtained with American populations. Practical courses such as languages and health-related topics were found to be the most popular; and Nearly a quarter (27%) of the respondents (in particular those who are well educated) expressed interest in peer teaching. The findings are important to understand LLL in the Chinese population and assist in the formulation of an appropriate LLL policy in Hong Kong. These findings also serve as a comparison for other countries trying to provide continuing education opportunities for its older citizens.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".