Identifying physical activity information needs and preferred methods of delivery of people with multiple sclerosis
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to examine the preferred sources and methods for acquiring physical activity information of individuals with multiple sclerosis (MS) using the Comprehensive Model of Information Seeking. A secondary objective was to explore the barriers and facilitators to physical activity information seeking. METHODS: Twenty-one participants diagnosed with MS participated in focus groups or telephone interviews. RESULTS: A direct content analysis of the transcripts revealed that individuals appeared to generally prefer receiving physical activity information during period of relapse and remission. Participants also had positive beliefs toward physical activity and a clear preference for a time when physical activity messages would be relevant. Receiving physical activity information from credible sources such as the MS Society of Canada, healthcare professionals and peers with MS was also deemed important. The Internet was a preferred source to receive information due to its accessibility, but it often was considered to lack credibility. The lack of physical activity information specific to MS is the greatest barrier for individuals with MS to learn about physical activity. CONCLUSIONS: Healthcare professionals, National MS Societies, and peers should work together to deliver specific and relevant physical activity messages the MS population. IMPLICATIONS FOR REHABILITATION: People with MS want more physical activity information from credible sources. Multiple vehicles of physical activity information delivery (i.e. healthcare providers, peers, MS Society) should be utilized. Physical activity information should be tailored to the individual with MS.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".