TAKING LEISURE SERIOUSLY: LEISURE-BASED INTERVENTIONS TO SUPPORT COGNITIVE HEALTH
Bibliographic record
Abstract
Older adults are looking for ways to increase their cognition and prevent age-related cognitive decline. In this symposium, we will assess whether participation in cognitively stimulating leisure activities improves cognition in older adults. A large number of epidemiological studies have indeed shown that being engaged in such activities is associated with better cognitive health in older adults. Thus, interventions involving leisure activities might help to prevent cognitive decline while at the same time being ecologically valid and easy to implement in the community. The symposium will present studies that have developed and tested leisure-based interventions meant to stimulate cognition in older adults. It will cover programs that rely on a variety of leisure activities, ranging from crafts, music and artistic production to technological learning and volunteering. Furthermore, the symposium will touch on major issues related to the use of leisure activities as a way to increase cognition. In addition to measuring the potential for these interventions to improve cognition, the symposium will address effects on well-being, the role of family members, the potential for web-based applications, the most effective intervention modalities and their effects on brain function.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".