Physical activity participation in Australians with multiple sclerosis: associations with geographical remoteness
Bibliographic record
Abstract
PURPOSE: Physical activity (PA) participation offers many benefits for persons with multiple sclerosis (MS). Persons with MS are significantly less active than the general population; however, there is insufficient evidence regarding the association between geographical remoteness and PA participation in persons with MS. We identify PA levels across levels of rurality in an Australian MS population. MATERIALS AND METHODS: The Australian MS Longitudinal Study collects regular survey data from persons with MS in Australia, including demographic, clinical, and health behavioural data. Physical activity engagement was identified with the International Physical Activity Questionnaire-short form and geographical remoteness was identified from participants' postcode using the Access and Remoteness Index for Australia. Hurdle regression analysis examined the relationship between remoteness and PA participation, and level of PA, after controlling for confounding. RESULTS: = 960), those living in more remote areas had, on average, higher levels of PA (RR 1.21; 89% HDPI estimate 1.11, 1.34). CONCLUSIONS: Physical activity promotion does not need to differ based on geographical location. Implications for rehabilitationAlmost one quarter of persons with MS in our study recorded no participation in any physical activity (PA).Healthcare practitioners are encouraged to include the promotion of PA as part of MS management.Physical activity participation is similar for persons with MS across different geographical locations.Physical activity promotion does not need to differ based on geographical location.
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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 | 0.005 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".