Identifying the needs and preferences of potential users of a digital platform to facilitate outdoor leisure physical activities for people with physical or sensory disabilities
Bibliographic record
Abstract
PURPOSE: Participation in outdoor leisure physical activities (OLPAs) benefits people with disabilities (PWDs). However, PWDs face multiple challenges to participation in OLPAs. Thus, an online platform is being developed to facilitate PWDs' access to adapted OLPAs. The aim of this study was to explore the needs and preferences of PWDs and the volunteers who support them during OLPAs regarding such an online platform. MATERIALS AND METHODS: A qualitative study was conducted with a descriptive interpretive approach. PWDs and volunteers who support them during OLPAs participated in semi-structured interviews. Data were analyzed following Braun and Clarke's five steps for thematic analysis. RESULTS: Sixteen PWDs and 15 volunteers participated in the study. Analysis of the interviews revealed five major themes the participants found important for the development of the platform: (1) Having several functionalities; (2) Offering the desired training; (3) Including information; (4) Pairing volunteers with PWDs; and (5) Displaying the information. CONCLUSION: The study highlights the key features future platforms should include to meet the needs of PWDs and volunteers and facilitate access to OLPAs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".