Reasons for not participating in scleroderma patient support groups: a comparison of results from the North American and European scleroderma support group surveys
Bibliographic record
Abstract
PURPOSE: Many people with scleroderma rely on peer-led support groups as a coping resource. Reasons for not attending support groups in scleroderma have been investigated only in North American participants. This study assesses reasons for nonattendance in European countries and compares results with previously published North American findings. MATERIALS AND METHODS: were compared between samples. RESULTS: = 242), the two items most commonly rated as (Very) Important reasons for nonattendance among 228 European participants were (1) already having enough support (57%), and (2) not knowing of any local scleroderma support groups (58%). Compared to North American non-attenders, European patients were significantly more likely to rate not knowing enough about what happens at support groups (46% vs 19%), not having reliable ways to get to meetings (35% vs 17%), and being uncomfortable sharing experiences with a group (22% vs 11%) as (Very) Important reasons for nonattendance. CONCLUSIONS: Improving access to European support groups, providing education about support groups and group leader training may encourage participation.IMPLICATIONS FOR REHABILITATIONRehabilitation professionals might help develop local support groups for people with systemic sclerosis (scleroderma) to address the lack of access to these groups for many patients.The need for transportation and limited local accessibility may also be addressed by implementing online systemic sclerosis support groups.Professionals in the field of rehabilitation may work with people with systemic sclerosis and patient organizations to provide education about support groups to improve support group attendance in Europe.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| 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".