Leisure Education and Later‐Life Planning: A Conceptual Framework
Bibliographic record
Abstract
Abstract Older adults with intellectual disability represent a growing segment of the elderly population in developed and, to some extent, in developing nations worldwide. A considerable body of research has addressed this burgeoning demographic over the past 20 years. Although some variations appear within etiological subgroups, the biological processes of aging and related concerns (e.g., changes in health status) are similar for people independent of whether a person has an intellectual disability. The unique life experiences of individuals with intellectual disabilities, however, introduce social and environmental factors and practices that affect healthy aging and life quality, but are less well understood. As such, later‐life planning is an accepted, although not always practiced, mechanism used and directed by adults without disabilities to plan for their futures in later life. Planning for this life stage among older adults with intellectual disabilities, if it is done at all, typically is a parent/family‐driven process with a limited scope of focus (e.g., guardianship, financial security). Drawing on previous research in the areas of later‐life planning and leisure education, the authors present a conceptual rationale for melding these two processes and propose principles and content elements that could facilitate the use of leisure education as a framework for holistically exploring later‐life options and issues.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".