The challenges of travelling after stroke have dramatically reduced my quality of life - a survey of a neglected traveller population
Bibliographic record
Abstract
Background: The tourism and holiday industries are multi-million dollar businesses and travellers with a disability are a diverse, neglected traveller population. Research into accessible or inclusive travel is in its infancy and there is limited research after stroke. Aims: To explore the travel experiences, motivations, patterns, and attitudes of stroke survivors. Methods: A cross-sectional survey collected demographic information, self-rated health, stroke-related information, travel patterns, modified Canadian Occupational Performance Measure, and included travel specific questionnaires. Results: Thirty-five stroke survivors (mean age 56.6 years) responded, four received assistance to complete the survey, and global disability scores ranged from none to moderately severe. Travel patterns changed significantly post-stroke including day trips through to long haul overseas travel (p < .001). The importance of travel was rated 7.8 out of 10, self-rated performance was 6.8, and satisfaction was 5.7. Performance was associated with self-rated health (rs = .519, p = .001) and global disability score (rs = -.444, p = .008). Satisfaction was associated with self-rated health (rs = .527, p = .001). Participants travelled to: places where they felt safe and secure; visit friends and relatives; be in control and free; and escape their usual environments. Reported attitudes and experiences of travel suggest they expected, and then experienced, challenges with access, requiring assistance of others, and difficulty obtaining travel insurance. Conclusion: Stroke survivors experience challenges in travel, consider travel important and are not satisfied with current performance. Personal and environmental factors influence this outcome and further research is required to co-develop solutions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".