“I never really thought that a virtual ride would be that good!”: Experiences of participants with disabilities in online leisure-time physical activity during COVID-19
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic has exacerbated the sedentary behavior and inactivity of people, including individuals with disability, who were already less active than their able-bodied counterparts. Therefore, it is particularly important to think about how to maintain and increase their leisure-time physical activity (LTPA). Online adaptive programs may represent a useful tool to do so. However, there is a little research focused on the health impacts of online LTPA. OBJECTIVE: This mixed-methods study aimed to explore the experiences of people with disabilities who participated in online adaptive LTPA along with the factors contributing to or limiting participation. METHOD: First, semi-structured interviews were conducted with 10 individuals participating in online adaptive LTPA offered by a community organization. Based on these interviews, a survey was developed and completed by 104 participants. RESULTS: The results of the study suggested that people with disabilities can get a variety of physical and emotional health benefits when participating in adaptive online LTPA, including a strong social benefit. Staff attitude and knowledge as well as the staff's ability to adapt to participant needs played important roles in facilitating participation. Greater access to equipment was needed. CONCLUSION: This study offers insights into how online LTPA could support the health-promoting behavior of people with disabilities during the pandemic and beyond.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.007 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".