Let’s Take A Walk: Exploring the Impact of an Inclusive Walking Program on the Physical and Mental Health of Adults with Intellectual Disability
Bibliographic record
Abstract
Background: People with intellectual disabilities experience health disparities and poorer health outcomes than people without disabilities. Increased physical activity has been found to reduce the impact of chronic health conditions among people with intellectual disabilities. Method: The current study explored the impact of an inclusive walking program on the physical and mental health of adults with intellectual disabilities. Let’s Take A Walk paired adults with intellectual disabilities, hereafter referred to as Community Walkers (n = 27), with college students to walk around a college campus twice a week for 45 minutes across 10 weeks. Data on mental health outcomes, specifically depression and anxiety, were collected from 24 Community Walkers across four-time points (pre-, mid-, post-, and 3-months following intervention), and data on physical health outcomes were collected across two-time points (pre- and post-intervention). Results: Community Walkers reported significant decreases in both depression and anxiety from pre to post-implementation. Particularly promising was clinically significant decreases in anxiety symptoms over the 10-week program. No differences were noted on Community Walkers’ measures of physical health. Conclusion: Inclusive walking programs are a valuable and promising mechanism for building social connections and inclusion and improving mental health for adults with intellectual 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".