Social support from exercise instructors in group physical activity programs for older adults
Bibliographic record
Abstract
Physical activity can facilitate successful aging. Social support is associated with engaging in physical activity. A potential source of support is physical activity instructors, as they can affect the social climate and provide direct support to participants. However, little is known about older adults' expectations for social support from instructors and specific behaviours they experience as supportive. This study examined older adults' experiences with social interactions with instructors in group exercise programs, and their perspectives on instructor behaviours that support participation. Guided by interpretive description methodology, we conducted ten focus groups with N=38 older adults in group exercise programs at city recreation centers (n=29 women, 9 men; Mage=69.5 years). Responses were analyzed in light of successful aging, age-friendly cities, and social support frameworks. Instructor supportiveness was a key influence in the decision to engage in a particular program. Instructor characteristics that participants identified as supportive included: knowledge of aging and physical activity; ability to deliver individualized feedback; and including adaptations within class activities. Participants felt especially supported in classes where instructors were both caring and challenging, while remaining inclusive of different levels of ability. Participants felt less engaged in classes with instructors who controlling, versus those who respected autonomy. Some participants wanted support from instructors to address interpersonal problems among participants, but acknowledged that instructors were not always equipped to do so. Results highlight the importance of understanding instructors as a source of support, and the need for guidance on how instructors can provide effective support in this context.Acknowledgments: Funding: Social Sciences and Humanities Research Council of Canada Insight Grant.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".