Nutrition Information Resources Used by People With Systemic Sclerosis and Perceived Advantages and Disadvantages: A Nominal Group Technique Study
Bibliographic record
Abstract
OBJECTIVE: Where people with systemic sclerosis (SSc) (or scleroderma) obtain diet and nutrition information to manage their disease is not known. Objectives were to identify 1) resources used by people with SSc for nutrition and diet information and 2) perceived advantages and disadvantages of resources. METHODS: We conducted nominal group technique (NGT) sessions in which people with SSc reported nutrition and diet information resources they have used and perceived advantages and disadvantages of accessing and using resources. Participants indicated whether they had tried each resource. They rated helpfulness and importance of possible advantages and disadvantages. Items elicited across sessions were merged to eliminate overlap. RESULTS: We conducted four NGT sessions (three English language, one French language; 15 total participants) and identified 33 unique information resources, 147 resource-specific advantages, and 118 resource-specific disadvantages. Resource categories included health care providers, alternative and complementary practitioners, websites and other media platforms, events, and print materials. The most common themes for advantages and disadvantages included quality and individualization of information and accessibility of resources in terms of cost, location, and comprehensibility. Information provided by medical professionals was regarded as most credible and can be obtained through books, articles, and websites if individual consultation is not easily accessible. Web-based information was considered highly accessible, although of variable credibility. In-person events may be an important source of health information for people with SSc. CONCLUSION: People with SSc obtain nutrition and diet information from multiple resources. They seek credible and accessible resources that provide SSc-specific and individualized information.
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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 | 0.011 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".