Physical Activity, Well-Being, and the Needs of Canadians with Disabilities during the COVID-19 Pandemic
Bibliographic record
Abstract
Background: This study examined self-reported physical activity (PA) participation, well-being, and perceived needs of Canadians with disabilities during the COVID-19 pandemic. In addition, we assessed physical and mental health and the extent to which pre-identified needs were being met or unmet. Methods: Two iterations of the COVID-19 Disability Survey were conducted during two pandemic timeframes: June–December 2020 (iteration 1, n = 599) and December 2020–September 2021 (iteration 2, n = 528). PA participation was assessed with the International Physical Activity Questionnaire. Physical and mental health were assessed with the PROMIS Global-10 questionnaire. A needs assessment was conducted on 11 needs pre-identified in partnership with community organizations. Results: Approximately 50% of respondents to both iterations reported that they did not do any moderate-vigorous intensity PA. While physical health was not different between timeframes, mental health was worse during iteration 2 than iteration 1 (p = 0.028). During both timeframes, access to recreation and leisure facilities was the greatest unmet need. Conclusion: These data highlight the low levels of PA and the perceived changes in PA, mental health, and recreational needs of Canadians with disabilities during the pandemic. The findings of the Survey were used to support policy change to remove barriers to PA participation for people with disabilities in Ontario.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".