Social network characteristics and their relationships with physical activity in children with multiple sclerosis
Bibliographic record
Abstract
Background Physical activity has been found to associate with improved health outcomes in children with multiple sclerosis (MS). Social networks may facilitate physical activity in children with MS. Objectives To estimate associations between social network characteristics and physical activity in children with MS compared to children with monophasic acquired demyelinating syndrome (mono-ADS). Methods Children with MS and mono-ADS recruited from the Hospital for Sick Children completed questionnaires on social network and physical activity. Descriptive and inferential analyses estimated differences between cohorts and correlations. Results Children with MS ( n = 16) and mono-ADS ( n = 22) did not differ in outcomes. Higher physical activity associated with larger social networks (r s = 0.681, p < 0.01), more Emotional Support (r s = 0.604, p < 0.05), Camaraderie (r s = 0.585, p < 0.05), more social network members with post-secondary education (r s = 0.680, p < 0.05), and members who exercise 3 to 4 times a week (r s = 0.744, p < 0.01). These associations were not found in children with mono-ADS. Conclusion Larger social network size, more emotional support and camaraderie, and more individuals with post-secondary education and who regularly exercise in the social network associated with higher physical activity in children with MS. Social network characteristics may help understand health behaviors in children 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.003 | 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".