OLDER ADULT ONLINE EXERCISE CLASSES DURING THE COVID-19 PANDEMIC: A SURVEY OF SERVICE PROVIDER PERSPECTIVESS
Bibliographic record
Abstract
Abstract COVID-19 rendered the availability of exercise facilities sporadic and online exercise programs subsequently became more common. This research explored online exercise classes delivered to older adults during the pandemic from the perspective of service providers. Sixty-seven service providers completed the survey (88% female). The majority (54%) of respondents had worked in the fitness industry for greater than 10 years, and 66% were fitness class instructors, while fewer were managers (9%) and personal trainers (8%). Three participants had experience providing online exercise classes prior to the pandemic, while 43 more had experience providing online exercise classes since the pandemic began. Of these 46 service providers, 87% offered classes live through Zoom. The majority (64%) offered classes through an organization, and 61% charged a fee for participants to take part. The most common type of class was a general fitness class (63%), followed by yoga and flexibility classes (39%), and strength training (17%). Regarding equipment used, weights were most frequently required (69%), followed by resistance bands (49%) and mats (44%). Most classes lasted 40–60 minutes (59%) and were low intensity (74%). Of the 21 respondents who did not provide online exercise classes, 43% indicated this was because of a lack of interest, and 19% cited not knowing how to use technology to deliver classes online, though most (71%) indicated they would consider offering online classes in the future. This research reveals the adaptability of service providers and may serve to inform the continued development of online exercise programs for 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".