Reasons for attending support groups and organizational preferences: A replication study using the North American Scleroderma Support Group Survey
Bibliographic record
Abstract
Peer-facilitated support groups are an important source for receiving disease-related information and support for people with systemic sclerosis (or scleroderma). A recent survey explored reasons for attending systemic sclerosis support groups in Europe and Australia and used exploratory factor analysis to group reasons for attendance into three main themes: (1) interpersonal and social support, (2) disease treatment and symptom management strategies, and (3) other aspects of living with systemic sclerosis. The objective of the present study was to replicate this study in a sample of patients from North America using confirmatory factor analysis. A 30-item survey was used to assess reasons for attendance and organizational preferences among systemic sclerosis patients in Canada and the United States. In total, 171 members completed the survey. In the confirmatory factor analysis, the three-factor model showed good fit to the data (χ 2 (399) = 646.0, p < 0.001, Tucker–Lewis index = 0.97, comparative fit index = 0.97, root mean square error approximation = 0.06). On average, respondents rated 22 (73%) of 30 items as “important” or “very important” reasons for attending support groups. Among organizational preferences, respondents emphasized the importance of the ability to share feelings and concerns, as well as educational aspects. Findings of our study suggest that reasons for attending support groups are similar for patients from Europe, Australia, and North America and that support groups should facilitate social support as well as disease education. These results inform the development of training programs for current and future systemic sclerosis support group leaders across the globe.
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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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".