The effects of recreation & leisure participation in older adults (55+)
Bibliographic record
Abstract
Recreation and leisure programs play a vital role in the lives of older adults (55+). Research indicates that more active types of activity, in particular, are positively associated with higher health-related quality of life (Jenkins, Pienta, & Horgas, 2002). The purpose of this project was to examine the effects of recreation and leisure participation on older adults’ (55+) quality of life. Four out of the five participants were female, and one of the participants was male. The participants ranged in age from 61-79, the mean age was 65. Participants were recruited from local Community Centres in Vancouver and Coquitlam and were enrolled (or had previously been enrolled) in community recreation and leisure programs. Semi-structured, one-to-one, 30 minute interviews were conducted. Participants were asked a number of questions about participating in these programs, the effects they have experienced through participating, and their quality of life. The interviews were then transcribed verbatim, analyzed, and descriptively coded to organize data into categories based on my research question. Findings revealed that research participants’ experienced positive effects from participating in recreation and leisure programs, especially noticing increased positive emotions, social well-being, physical health, and psychological well-being, which contributed to an increase in participants’ quality of life. Key words: Community recreation, leisure, older adults, quality of life.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".