Identifying barriers and facilitators to physical activity for people with scleroderma: A nominal group technique study
Bibliographic record
Abstract
Abstract Purpose To identify physical activity barriers and facilitators experienced by people with systemic sclerosis (SSc; scleroderma). Materials and Methods We conducted nominal group technique sessions with SSc patients who shared barriers to physical activities, barrier-specific facilitators, and general facilitators. Participants rated importance of barriers and likelihood of using facilitators from 0-10, and indicated whether they had tried facilitators. Barriers and facilitators across sessions were subsequently merged to eliminate overlap; edited based on feedback from investigators, patient advisors, and clinicians; and categorized. Results We conducted nine sessions (n=41 total participants) and initially generated 181 barriers, 457 barrier-specific facilitators, and 20 general facilitators. The number of consolidated barriers (barrier-specific facilitators in parentheses) for each category were: 14 (61) for health and medical; 4 (23) for social and personal; 1 (3) for time, work, and lifestyle; and 1 (4) for environmental. There were 12 consolidated general facilitators. The consolidated items with ≥ 1/3 of participants’ ratings ≥ 8 were: 15 barriers, 69 barrier-specific facilitators, and 9 general facilitators. Conclusions People with SSc reported many barriers related to health and medical aspects of SSc and several barriers in other categories. They reported facilitators to remain physically active despite the barriers. Implications for Rehabilitation People with scleroderma experience difficulty being physically active due to the diverse and often severe manifestations of the disease, including involvement of the skin, musculoskeletal system, and internal organs. In addition to regular care of scleroderma-related symptoms, patients overcome many exercise challenges by selecting physical activities that are comfortable for them, adjusting the intensity and duration of activities, adapting activities, and using adapted equipment or other materials to reduce discomfort. Rehabilitation professionals should help people with scleroderma to tailor activity options to their capacity and needs when providing care and advice to promote physical activity.
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 | 0.020 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".