Support and information needs of people with systemic sclerosis by time since diagnosis: A cross-sectional study
Bibliographic record
Abstract
Background: How support and informational needs of people with systemic sclerosis (SSc) may differ by time since diagnosis is not known. Our objective was to determine if informational and support needs of recently diagnosed individuals with systemic sclerosis differ from people diagnosed for longer periods of time. Methods: The North American Scleroderma Support Group Members survey included 30 items on reasons for attending support groups. Respondents were classified by time since diagnosis of 0–3 years, 4–9 years or 10+ years. Survey item responses were dichotomized into Not Important or Somewhat Important versus Important or Very Important. We conducted Chi-square tests with Hochberg’s Sequential Method to identify item differences by time since diagnosis. Results: A total of 175 respondents completed the survey. Most support needs were rated as Important or Very Important by respondents, regardless of disease duration, particularly needs related to interpersonal and social support (10 items; median 81%) and learning about disease treatment and management strategies (11 items; median 82%). Discussing other aspects of living with systemic sclerosis (e.g. spirituality, discussing disease with family and friends) was rated lower (9 items; 44%). Respondents with 0–3 years since diagnosis were the highest on 29 of 30 items. Respondents with 0–3 years since diagnosis were significantly higher on items related to discussing medical care and 4 items on other aspects (spirituality, talking with family and friends, financial issues, sexual issues). Conclusion: People with systemic sclerosis have a wide range of information and support needs, regardless of their disease duration, but people with recent diagnoses have greater needs.
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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 | 0.001 | 0.003 |
| 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".