Education and Measurement are the Top Priorities to Advance Physical Literacy for Individuals with Physical Disabilities
Bibliographic record
Abstract
Background: Most individuals with physical disabilities do not meet physical activity recommendations, which may negatively impact quality of life. Physical literacy is a concept that considers the key elements necessary to support lifelong physical activity. Limited attention has been directed towards physical literacy for individuals with physical disabilities. Objectives: To obtain expert consensus on strategic priorities to delineate the next steps on physical literacy for individuals with physical disabilities. Methods: The Collaborative Prioritized Planning Process was followed during a two-day online consensus meeting with experts in disability and physical activity. This systematic four-step process involved: 1) knowledge synthesis before the meeting, 2) challenge identification and prioritization, 3) solution identification, consolidation and prioritization, and 4) action planning. Results: Thirty-one experts participated in the meeting. Five challenges related to physical literacy for individuals with physical disabilities were prioritized. The following solutions were suggested: developing a massive online open course, creating a physical literacy measurement toolkit, developing a physical literacy resource portal, creating a national database of physical literacy outcomes, and redefining an existing international consensus statement for physical literacy to be more inclusive. Conclusions: Collaborations between experts are needed to advance the research in physical literacy for people with disabilities through education and measurement.
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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 | 0.056 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".