Quality of Physical Activity Participation Among Adults with Disabilities Through Pandemic Restriction
Bibliographic record
Abstract
Background. Physical activity (PA) is essential for maintaining well-being in adults with disabilities. This population experienced reduced PA during the COVID-19 pandemic; yet, the impact on quality of PA participation remains unclear. Purpose. This secondary analysis explored how pandemic restrictions impacted six experiential dimensions of quality of PA participation among adults with disabilities. Methods. An exploratory sequential mixed-methods design, including semi-structured interviews ( n = 10) and self-reported surveys ( n = 61), was conducted in May-2020 and February-2021. Quality of PA participation was measured using the Measure of Experiential Aspects of Participation (MeEAP). Participants included community-dwelling adults over 19 years of age (mean 59.2 ± 14.0 years) living with stroke, spinal cord injury, or other physical disabilities. Findings. Directed content analysis identified three themes related to adjusting PA participation for restrictions, motivation barriers, and valuing social support. These themes highlighted five factors, such as resilience, as potential quantitative predictors of quality of PA participation. While paired correlations with MeEAP scores were observed, these factors were not statistically predictive in multiple regression analysis (adjusted R 2 = −0.14, F(10,50) = 0.92, p = .53). Implications. The interplay between Meaning, Autonomy, Engagement, and Belongingness dimensions of quality of PA participation was complex, with an emphasized role for mental health, in adults with disabilities.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".