Contribution of Generative Leisure Activities to Cognitive Function in Elderly Sri Lankan Adults
Bibliographic record
Abstract
OBJECTIVES: To examine the unique contribution of generative leisure activities, defined as activities motivated by a concern for others and a need to contribute something to the next generation. DESIGN: Cross-sectional survey. SETTING: Peri-urban and rural area in southern Sri Lanka. PARTICIPANTS: Community-dwelling adults aged 60 and older (N = 252). MEASUREMENTS: The main predictors were leisure activities, grouped into generative, social, or solitary. The main outcome was cognitive function, assessed using the Montreal Cognitive Assessment (MoCA) and the Informant Questionnaire on Cognitive Decline in the Elderly (IQCODE). RESULTS: More-frequent engagement in generative leisure activities was associated with higher levels of cognitive function, independent of the effect of other social and solitary leisure activities. In a fully adjusted model combining all three leisure activities, generative activities independently predicted cognitive function as measured using the MoCA (β = 0.47, 95% confidence interval (CI) = 0.11-0.83) and the IQCODE (β = -0.81, 95% CI = -1.54 to -0.09). In this combined model, solitary activities were also independently associated with slower cognitive decline using the MoCA (β = 0.40, 95% CI = 0.16-0.64) but not the IQCODE (β = -0.38, 95% CI = -0.88-0.12); the association with social activities did not reach statistical significance with either measure. These associations did not differ meaningfully according to sex. CONCLUSION: Generative leisure activities are a promising area for the development of interventions aimed at reducing cognitive decline in elderly adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".