DANCE IS FOR EVERYONE: A COMPREHENSIVE STUDY OF COMMUNITY-BASED DANCE PARTICIPATION FOR OLDER ADULTS
Bibliographic record
Abstract
Objective: As the population ages, increasing physical activity (PA) among older adults (65+) is crucial for enhancing their health; however, only 40% meet the recommended PA guidelines (Statistics Canada, 2023). Dance offers an engaging form of physical activity that can enhance holistic well-being (Santos et al., 2020). Despite these benefits, barriers to participation persist. This study investigated the effects and experiences of older adults participating in community- based dance as a form of PA, including the various factors influencing their engagement. Methods: In Study I, participants completed a questionnaire which included five scales: the International Physical Activity Questionnaire Short Form (IPAQ-S), the Activities-specific Balance Confidence Scale (ABC), the Short Happiness and Affect Research Protocol (SHARP), the Functional Activities Questionnaire (FAQ) and the UCLA 3-Item Loneliness Scale. Study II utilized interpretive phenomenology to guide one-on-one semi-structured interviews. Results: Questionnaire analysis revealed older adults in dance exhibited moderate to high PA levels (IPAQ-S), strong physical functioning and low fall risk (ABC), strong emotional health (SHARP), no impaired functioning or cognitive impairment (FAQ), and low levels of loneliness (UCLA3). Interviews highlighted three intrapersonal (e.g., physical impairments), two interpersonal (e.g., poor instructors), and four community-level barriers (e.g., availability), while seven intrapersonal (e.g., adaptable), two interpersonal (e.g., social connections), and four community-level facilitators (e.g., inexpensive fees) enhanced participation. Conclusions: This study highlights how community-based dance enhances the holistic health of older adults, motivating them to engage in a form of PA that enriches overall quality of life. By identifying key factors influencing and hindering dance participation, the research offers valuable insights for designing effective dance programs tailored to 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".