Buffering effects of leisure self‐determination on the mental health of older adults
Bibliographic record
Abstract
Leisure self‐determination was tested for its capacity to buffer the effects of life stress on the level of depression of older adults. A direct association between leisure‐self‐determination and level depression was also tested. A sample of 152 individuals aged 49 years and over completed a questionnaire which included measures of stress, leisure self‐determination, and depression. Hierarchical multiple regression analysis incorporating an interaction component to represent the buffering effect was used to analyze the data. Higher levels of leisure self‐determination were significantly associated with lower levels of depression regardless of life stress. Leisure self‐determination also acted as a buffer of the association between life stress and depression. The study has significant theoretical and practical implications. Theoretically, it supports the stress buffering hypothesis of Coleman and Iso‐Ahola (1993) when applied to a sample of older adults. The practical implications of the empirical evidence focus on the importance of fostering leisure self‐determination dispositions through leisure practices, policies, and leadership styles that facilitate and support older adult autonomy in leisure experiences.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".