Factors associated with high levels of physical activity among adults with intellectual disability
Bibliographic record
Abstract
The aim was to identify factors associated with physical activity participation among active (i.e. more than or equal to 10,000 steps per day) individuals with intellectual disability. Staff at day program and supported employment organizations were asked to identify individuals they believed were physically active. To verify participants were active, 7-day pedometer data were collected. Using these data, 13 participants met the inclusion criterion of 10,000 steps per day from 37 individuals identified by staff. Participants (n=13) ranged in age from 18 to 46 years (mean=34, SD= 8) and seven were male. In-depth semistructured interviews were conducted to explore the environmental and social supports for these high levels of physical activity. Three major themes were identified from content and thematic analyses. Key individuals and organizations were important in fostering initial interests and the development of skills. Coaches, staff and parents were important in 'showing them how.' For example, how to vacuum or stack produce, how to bowl, how to use weight training equipment or helping to plan a safe walking route. Motivation for initial and ongoing participation was associated with friendship and social connection. Practical support was needed for continued participation. In particular, transportation and affordable activities were very important. Fostering practical skills, supporting the social aspects of physical activity and keeping activities low cost are important enabling and reinforcing factors for physical activity among active persons with intellectual disability.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".