Effectiveness of a Driving and Community Mobility Intervention for Teens and Young Adults With Autism Spectrum Disorder
Bibliographic record
Abstract
IMPORTANCE: Driving and community mobility (DCM) is key to supporting independence in teens and young adults (TYA) with autism spectrum disorder (ASD). OBJECTIVE: To determine whether an intervention focused on DCM can effectively improve DCM knowledge and skills in TYA. DESIGN: A pretest-posttest design using the same intervention and outcome measures. SETTING: University research setting. PARTICIPANTS: Thirty-eight participants with ASD completed the program (M age = 17.76 yr, SD = 3.58). Twenty-six (68%) were male, and 12 (32%) were female. INTERVENTION: A 5-day intensive intervention using group and individualized strategies, including driving simulation, focused on improving performance skills needed for DCM. OUTCOME AND MEASURES: Total and category scores of the Performance Analysis of Driving Ability (P-Drive) were analyzed using a repeated-measures analysis of variance measuring time (pretest vs. posttest), gender, and year of intervention. Paired t tests were used to determine the outcomes of the Canadian Occupational Performance Measure (COPM), anxiety measure and perception survey. RESULTS: Results indicated a significant main effect for time (p < .001) and year (p < .036), but not gender (p < .26), with no significant interaction effects, supporting the fidelity of the intervention. The COPM showed significant changes (p ≤ .001) in both performance and satisfaction, as well as a reduction in anxiety (p = .008). CONCLUSIONS AND RELEVANCE: Outcomes suggest that the intervention successfully improved DCM knowledge and skills for TYA. This adds new evidence that an occupational therapy intervention specific to TYA with ASD is effective in developing independence in DCM. Plain-Language Summary: Driving is the primary mode of community mobility in North America and other Western countries. Adults with autism spectrum disorder (ASD) who have a means of community mobility have a chance of employment that is 5 times greater than that for those who do not. Driving and community mobility are key to supporting independence among teens and young adults with ASD. However, teens and young adults with ASD have significantly lower rates of getting a driver's license. The results of this study show that occupational therapy practitioners can play a critical role in addressing driving and community mobility. Occupational therapy interventions that are specifically designed for and unique to each teen and young adult with ASD can effectively improve driving and community mobility skills and increase independence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".