Reasons for Not Participating in Scleroderma Patient Support Groups: A Cross‐Sectional Study
Bibliographic record
Abstract
OBJECTIVE: Peer-led support groups are an important resource for many people with scleroderma (systemic sclerosis; SSc). Little is known, however, about barriers to participation. The objective of this study was to identify reasons why some people with SSc do not participate in SSc support groups. METHODS: A 21-item survey was used to assess reasons for nonattendance among SSc patients in Canada and the US. Exploratory factor analysis (EFA) was conducted, using the software MPlus 7, to group reasons for nonattendance into themes. RESULTS: [150] = 302.7; P < 0.001; Comparative Fit Index = 0.91, Tucker-Lewis Index = 0.88, root mean square error of approximation = 0.07, factor intercorrelations 0.02-0.43). The 3 identified themes, reflecting reasons for not attending SSc support groups were personal reasons (9 items; e.g., already having enough support), practical reasons (7 items; e.g., no local support groups available), and beliefs about support groups (5 items; e.g., support groups are too negative). On average, respondents rated 4.9 items as important or very important reasons for nonattendance. The 2 items most commonly rated as important or very important were 1) already having enough support from family, friends, or others, and 2) not knowing of any SSc support groups offered in my area. CONCLUSION: SSc organizations may be able to address limitations in accessibility and concerns about SSc support groups by implementing online support groups, better informing patients about support group activities, and training support group facilitators.
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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 | 0.003 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".