How older adults negotiate constraints to leisure-time physical activities following a heart attack
Bibliographic record
Abstract
Leisure has been found to improve later-life well-being and help people cope with life changes (Dupuis & Alzheimer [2008]. Leisure and ageing well. World Leisure Journal, 50(2), 91–107. https://doi.org/10.1080/04419057.2008.9674538; Michèle et al., [2019]. Social and leisure activity profiles and well-being among the older adults: A longitudinal study. Aging and Mental Health, 23(1), 77–83. https://doi.org/10.1080/13607863.2017.1394442). Leisure activities, including leisure-time physical activity, may significantly affect healthy aging and improve health-related quality of life among older persons. However, there is a lack of literature revealing how older adults negotiate the constraints of leisure-time physical activity following a heart attack. This research aimed to explore the constraints of leisure-time physical activity participation and the process of constraints negotiation among older people who have had a heart attack. In this qualitative descriptive study, data were collected from 10 participants through face-to-face interviews. Thematic analysis was used to analyse the data. Participants experienced intrapersonal, interpersonal, and structural constraints to leisure-time physical activity. However, participants negotiated these constraints when they were motivated to be physically active following the heart attack.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".