‘Promoting wellness, having fun, and creating community’: a dance instructor’s pedagogical practices and perspectives on the influence of community dance classes for older adults
Bibliographic record
Abstract
Canada’s aging population highlights the critical need to identify physical activity programming that has the potential to positively contribute to one’s aging journey. Dancing is one type of physical activity that can offer the opportunity to be with others, move to music, and encourage creativity and personal expression. Dance instructors may influence older adults’ experiences with, and outcomes following, participation in dance classes. This study examined the pedagogical practices of an instructor with extensive experience teaching community dance classes for older adults, and their perspectives on participant experiences and outcomes through the lens of successful aging, physical literacy, and embodiment theories. The dance instructor participated in three semi-structured interviews over a 10-month period. Utilizing reflexive thematic analysis, four themes were generated: (1) the coffee and cookies might be the most important part, (2) everyone can walk away feeling successful at something, (3) a space where class attendees can be creative and expressive, and (4) supporting physical, emotional, and psychological needs. Dancing is a physical and performative activity that can positively contribute to various aspects of one’s aging and physical activity journey when instructors purposefully employ behaviors that consider multidimensional aspects of health.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| 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".