Arts Engagement Programs improves Health in Community-Dwelling Older Adults
Bibliographic record
Abstract
Abstract We examined how different arts engagement programs compared to control affect health in community-dwelling older adults. 64 adults(71.3 + 4.6years; Dance n=23, Music n=17, Control, n=24) took part in free Dance(Ballroom), Music(Ukulele), or Control(Active social conversation) sessions 2 times/week for 10 weeks. We assessed cognition(Montreal-Cognitive-Assessment-MoCA), physical(Short-Physical-Performance-Battery-SPPB), and Health-Related Quality-of-Life(HRQoL-SF-20) 3 times: (1) before(pre), (2) at the end of 10 weeks(post-1) and (3) 1-month after intervention(post-2). Separate 3(Time)x3(Group) ANOVAs and Bonferroni-pairwise-comparisons examined changes across groups and time(p<.05). Participants’ physical health improved equally across groups(p=.4) and over time(p<.001), specifically from pre(10.5 + 1.4) to post-1(10.7 + 1.3; p=.002), and pre to-post-2(11.3 + 1.0;p<.001). Participants’ cognition improved equally across groups(p=.6) and over time(p<.001) from pre(26.3 + 2.8) to post-1(27.3 + 2.5; p=.002), and pre-to-post-2(27.5 + 2.5;p=.001). Participants’ HRQoL remained similar over time(p=.6) and across groups(p=.7). Overall, participants’ health improved after taking part in arts engagement and social conversation programs. Study findings offer insights about successful implementation of arts-engaged programs in community-dwelling older adults.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".