OLDER PEOPLE'S ACTIVITY PARTICIPATION (OPAP): FACTOR STRUCTURE AND PSYCHOMETRIC PROPERTIES OF A SCALE
Bibliographic record
Abstract
Abstract Older people’s participation in activities is critical to their health and well-being. Active lifestyle in old age reduces the risk of mortality, prevents chronic diseases, promotes physical and mental wellbeing, and is conducive to active and healthy aging. While various measures of participation have been proposed, a holistic measure that includes both social and individual activity participation is currently unavailable. To enable lifestyle medicine recommendations, we developed the Older People’s Activity Participation (OPAP) scale to understand the constituent factors of older people’s everyday activities. This study examined OPAP’s internal consistency using Cronbach’s alpha, convergent validity using regression analysis, as well as factor structure using exploratory and confirmatory factor analyses in a dense urban setting. Preliminary items assessed engagement in 27 health-related, fitness, recreational, social, productive, and cognitive activities, and were administered to 270 community-dwelling adults aged 50 and older in Singapore. The 17-item OPAP showed acceptable internal consistency (Cronbach’s alpha= .69), and demonstrated convergence with a validated measure of social cohesion (B=.27, p<.001) and Instrumental Activities of Daily Living (B=.32, p<.001). It has a 5-factor structure, namely socializing (alpha=.63), physical training (alpha=.56), listening (alpha=.69), home-making (alpha=.59), and outing (alpha=.58). Acceptable model fit was obtained (RMSEA=0.48, SRMR=0.06, CFI=.90). The OPAP scale is valid and reliable to assess activity participation in Asian older adults. It can be a useful tool to understand older people’s everyday life and lifestyles, relationships between activity participation and health outcomes, and further guide the development and use of effective interventions to promote active and healthy aging.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".