Correlates of participation in meaningful activities among people with multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: To explore the associations between impairments, self-management self-efficacy, self-management behaviors, and environmental factors and their role in predicting participation in meaningful activities among people with multiple sclerosis. DESIGN: Online cross-sectional survey. SUBJECTS/PATIENTS: Randomly selected individuals (n = 335) from a large multiple sclerosis patient registry. METHODS: Participation in activities that are meaningful to the individual was measured with Community Participation Indicators (CPI), the dependent variable. Independent variables included symptom severity, activities of daily living limitations, cognitive problems, stages of change for physical activity, nutritional behaviors, self-efficacy, and environmental barriers. A backwards selection regression analysis was used to compare the relative contributions of independent variables in predicting the CPI. A path analysis was conducted to explore the associations between independent variables and their direct and indirect effects on the CPI. RESULTS: The final regression model included self-management self-efficacy (β = 0.12), environmental barriers (β = -0.16), cognitive problems (β = -0.22), and stages of change for physical activity (β = 0.12). Path analysis indicated that impairments and environmental barriers might negatively influence self-management self-efficacy. Self-management self-efficacy might have indirect effects on the CPI via engagement in self-management behaviors. CONCLUSION: Future research should explore whether interventions that promote self-management self-efficacy can facilitate participation in meaningful activities.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".