Identifying preferred format and source of exercise information in persons with multiple sclerosis that can be delivered by health‐care providers
Bibliographic record
Abstract
BACKGROUND: There is increasing recognition of the benefits of exercise in individuals with multiple sclerosis (MS), yet the MS population does not engage in sufficient amounts of exercise to accrue health benefits. There has been little qualitative inquiry to establish the preferred format and source for receiving exercise information from health-care providers among persons with MS. OBJECTIVE: We sought to identify the desired and preferred format and source of exercise information for persons with MS that can be delivered through health-care providers. SETTING AND PARTICIPANTS: Participants were adults with MS who had mild or moderate disability and participated in a range of exercise levels. All participants lived in the Midwest of the United States. METHODS: Fifty semi-structured interviews were conducted and analysed using thematic analysis. RESULTS: Two themes emerged, (i) approach for receiving exercise promotion and (ii) ideal person for promoting exercise. Persons with MS want to receive exercise information through in-person consultations with health-care providers, print media and electronic media. Persons with MS want to receive exercise promotion from health-care providers with expertise in MS (ie neurologists) and with expertise in exercise (eg physical therapists). CONCLUSIONS: These data support the importance of understanding how to provide exercise information to persons with MS and identifying that health-care providers including neurologists and physical therapists should be involved in exercise promotion.
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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 | 0.023 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".