Improving the participation of youth with physical disabilities in community activities: An interrupted time series design
Bibliographic record
Abstract
BACKGROUND/AIM: Youth with physical disabilities experience restrictions to participation in community-based leisure activities; however, there is little evidence about how to improve their involvement. This study examined whether an intervention to remove environmental barriers and develop strategies using a coaching approach improved youth participation in leisure activities. METHODS: An Interrupted Time Series design was employed, where replication of the intervention effect was examined across individualised participation goals and across participants. Six adolescents with a physical disability participated in a 12-week intervention. An occupational therapist worked with each youth and his/her family to set three leisure goals based on problems identified using the Canadian Occupational Performance Measure (COPM). A coaching approach was used to collaboratively identify and implement strategies to remove environmental barriers. Interventions for each goal were introduced at different time points. Outcomes were evaluated using the COPM. RESULTS: Improvements in COPM performance scores were clinically significant for 83% of the identified activities; an average change of 4.5 points in the performance scale (SD = 1.95) was observed. Statistical analysis using the celeration line demonstrated that the proportion of data points falling above the line increased in the intervention phase for 94% of the activities, indicating a significant treatment effect. CONCLUSIONS: This study is the first to examine an intervention aimed at increasing leisure participation by changing only the environment. The results indicate that environment-focussed interventions are feasible and effective in promoting youth participation. Such findings can inform the design of a larger study and guide occupational therapy practice.
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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 | 0.019 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".