Access denied: the shortage of digitized fitness resources for people with disabilities
Bibliographic record
Abstract
PURPOSE: The COVID-19 pandemic has drastically impacted every aspect of life, including how people exercise and access fitness resources. Prior to COVID-19, the global burden of disease attributable to sedentary behavior disproportionately affected the health of people with disabilities (PWD). This pre-existing gap has only widened during COVID-19 due to limited disability-friendly digital exercise resources. The purpose of this work is to examine this gap in accessibility to digital fitness resources, and re-frame the notion of accessibility to suit the contemporary context. MATERIALS AND METHODS: Using machine learning, video titles/descriptions about home exercise ordered by relevance populated on YouTube between 1 January 2020 and 30 June 2020 were examined. RESULTS: Using the search terms, "home exercise," "home-based exercise," "exercise no equipment," "workout no equipment," "exercise at home," or "at-home exercise," 700 videos ordered by relevance included 28 (4%) that were inclusive of participants with disabilities. Unfortunately, most digital fitness resources are therefore inaccessible to PWD. The global pause the pandemic has induced may be the right moment to construct a comprehensive, indexed digital library of home-based fitness video content for the disabled. There is a further need for more nuanced understandings of accessibility as technological advancements continue.Implications for RehabilitationPhysical activity is incredibly important to the quality of life and health of all people.Physical activity levels, however, remain lower among persons with disabilities.Access to disability-friendly resources remains a challenge and worsened by the circumstances of COVID-19 due to an apparent lack of digital fitness resources for persons with disabilities.A broader and comprehensive definition of accessibility must recognize digital advances and access to physical activity for persons with disabilities must feature digital resources.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.005 |
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".