Factors influencing exercise and sport participation among adults living with spinal cord injuries in Japan
Bibliographic record
Abstract
People living with spinal cord injuries (SCI) are less likely to participate in sports and exercise than the general population and people with other chronic conditions. A comprehensive understanding of influences on physical activity participation is needed in order to develop effective physical activity-enhancing interventions. Research on physical activity barriers and facilitators has been conducted in English-speaking and European countries, but not in Japan. The purpose of this qualitative study was to explore influences on exercise and sport participation experienced by Japanese people living with SCI. Semi-structured interviews were conducted with 9 adults living with SCI in Japan (5 men and 4 women; mean age = 52 ± 13yrs). Using pragmatism and the COM-B (Capability, Opportunity, Motivation-Behavior) model as guiding frameworks, the data were analyzed with a thematic analysis and directed content analysis. Two principal themes were identified which provide broad insights on the physical activity influences experienced by Japanese people with SCI: participation depends on meeting someone, and disablism limits participation. Content analysis showed that participants experienced physical activity influences related to all six dimensions of the COM-B model, but especially barriers related to physical opportunity, physical capability, and social opportunity. These findings inform three key recommendations to improve sport and exercise participation among people living with SCI in Japan: improve access to information, create opportunities for peer connections, and improve and enforce accessibility policies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".