Using GPS and Self-Report Data to Examine the Relationship Between Community Mobility and Community Participation Among Autistic Young Adults
Bibliographic record
Abstract
IMPORTANCE: Community participation of autistic adults is important for health and well-being. Many clinical efforts and interventions aim to enhance community participation in this population. OBJECTIVE: To empirically examine the relationship between community participation and community mobility. DESIGN: A randomized controlled trial using data from baseline and 4- to 6-wk follow-up. SETTING: Community organizations serving autistic adults in Philadelphia. PARTICIPANTS: Sixty-three autistic young adults with data on community mobility and participation from a prior study on public transportation use. OUTCOMES AND MEASURES: Participants were tracked with GPS-enabled cell phones over a 2-wk period. A spatiotemporal data mining algorithm was used to compute the total number of destinations, nonhome destinations, unique destinations, percentage of time spent outside the home, and median daily activity space area from the GPS data. The Temple University Community Participation measure was used to collect self-report data in 21 different areas, and total amount, breadth, and sufficiency of participation were calculated. RESULTS: Moderate and statistically significant associations were found between community mobility and participation variables at baseline and follow-up. However, changes in community mobility were not related to changes in community participation. CONCLUSION: Health policymakers and providers should consider community mobility as a factor that can affect community participation in autistic individuals. Plain-Language Summary: Lower levels of community participation among autistic young adults affect health outcomes and overall quality of life. Community mobility is often a barrier to community participation. An understanding of the relationship between community mobility and community participation can lead to occupational therapists tailoring specific interventions and policies that support autistic young adults to engage in important life activities within the community.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".