A qualitative examination on the implementation of participatory “A”rt-based activity on “Health” of older community-dwellers: what worked for the Singapore A-Health Intervention?
Bibliographic record
Abstract
Introduction: Art and museum-based interventions are gaining increasing recognition for their potential as low-risk activities for older adults, offering numerous physical, cognitive, and emotional benefits. However, there remains a dearth of knowledge regarding the science of implementation as well as the factors and processes that contribute to their effectiveness from the perspectives of intervention participants. Methods: The current research draws on the qualitative evaluation data obtained from a larger mixed-method randomized control trial that evaluated a standardized Participatory "A"rt-Based Activity On "Health" of Older Community-Dwellers-the Singapore A-Health Intervention. Adopting a participatory action research approach, the primary objective is to critically examine the lived experiences and health impact of the Singapore A-Health Intervention with a secondary objective to uncover strategies for optimized implementation outcomes. All 56 participants who completed the intervention filled out a program evaluation survey and a nested sample of 30 participants completed a series of acceptability focus groups. Results: = 1.43). Thematic analysis with a grounded theory approach on the qualitative focus group data revealed three interrelated themes detailing how the Singapore A-Health Intervention contributed to positive health and wellbeing outcomes (1. A-Health Experience, 2. Wellbeing Outcomes, 3. Enabling Factors) and nine subthemes (1a. Intellectual Stimulation, 1b. Positive Stress, 1c. Peer Interaction, 2a. Interpersonal Bonds, 2b. Personal Growth, 2c. Mindful Living, 3a. Integrated Support, 3b. Session Design, 3c. Mode of Engagement). Discussion: This investigation provides important insights to the Singapore A-Health intervention's effectiveness for enhancing wellbeing among older adults, as well as the factors that enable successful program implementation. These findings offer a culturally unique perspective on the benefits of art and museum interventions, while underscoring the imperative need for strong partnership and collaborations among community stakeholders in supporting the health and wellbeing of ageing populations.
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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 | 0.022 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".