A Delphi study on environmental factors that impact work and social life participation of individuals with multiple sclerosis in Austria and Switzerland
Bibliographic record
Abstract
PURPOSE: This study aimed to gain knowledge about environmental factors (EFs) that impact work and social life participation of people with multiple sclerosis (MS) in Austria and Switzerland to extend the knowledge of participation and to identify key areas for measuring participation. METHOD: A three-round Delphi study was conducted defining patients as experts. In the 1st round, qualitative data was gathered through questionnaires, analyzed with content analysis, and factors were assigned to EFs as classified in the ICF. In the 2nd and 3rd round, experts judged EFs according to its relevance to obtain consensus (cut-off 75%). Categories were ranked on a scale from mostly important to important. RESULTS: One hundred and twelve Austrian and 109 Swiss experts were recruited. The content analysis revealed 768 EFs. The study resulted in a list of 176 consensus factors for Austria and 177 Switzerland. Five categories revealed to be highly important, 12 moderately important, 6 fairly important, and 10 important. CONCLUSIONS: This study indicates that participation in work or social life is influenced by physical, social, attitudinal, and policy factors. Consensus factors afford insights into areas for consideration in the development of participation outcome measurements and support a comprehensive and inclusive rehabilitation approach.
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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 | 0.023 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".