The little intervention that could: creative aging implies healthy aging among Canadian seniors
Bibliographic record
Abstract
OBJECTIVES: Through a process of 'creative ageing', there is increased interest in how active participation in the arts can help promote health and well-being among seniors. However, few studies have quantitatively examined the benefits of a foray into artistic expression, and even fewer employ rigorous identification strategies. Addressing this knowledge gap, we use a series of quantitative techniques (ordinary least squares and quantile regression) to analyze the impact of an arts-based intervention targeting the elderly. METHODS: Recruited from Saint John, New Brunswick (a city of about 125,000 people in Eastern Canada), 130 seniors were randomly assigned to the programme, with the remaining 122 serving as the control. This intervention consisted of weekly 2-h art sessions (i.e. drawing, painting, collage, clay-work, performance, sculpting, and mixed media), taking place from January 2020 until April 2021. RESULTS: Relative to the control group, the intervention tended to reduce participant loneliness and depression, and improve their mental health. Outcomes were more evident toward the latter part of the programme, were increasing in attendance, and most efficacious among those with initially low levels of well-being. CONCLUSION: These findings imply that creative ageing promotes healthy ageing, which is especially noteworthy given COVID-19 likely attenuated our results.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".