Life with a rare chronic disease: the scleroderma experience
Bibliographic record
Abstract
BACKGROUND: Little is known about the experience of living with a rare disease and how people with rare diseases cope with not only the disease but also the reactions of others. Scleroderma is a rare chronic connective tissue disease that results in fibrotic changes involving all or some organs of the body. The two types of scleroderma are systemic scleroderma, which involves the skin and internal organs and is the more serious type, and local scleroderma, which attacks the skin and surrounding tissues. Some people with scleroderma have signs that are visible to outsiders, while others have invisible signs. Having this chronic condition and being different from the general population may subject people with scleroderma to stigmatization by others. AIM: The aim of this study was to understand, from the individual's perspective, the experience of living with scleroderma. METHOD: Focus group interviews were conducted with two groups of individuals with scleroderma. Because of the rarity of the disease and the illness of the participants, only two groups were held. The same questions were asked of both groups. A moderator and assistant guided the groups. FINDINGS: Data analysis revealed five themes: physical manifestations, disclosure/non-disclosure to others, living, being normal and facing the future. The data are discussed in light of participants' having visible signs, invisible signs and the rarity of their condition. For those with visible signs, disclosure was automatic. They were conscious of being different from others without scleroderma. Those with invisible signs managed their disease information in such a way as to minimize the stigma of being different. The rarity of the disease added the problem of others not understanding their difficulties. Those who disclosed their disease not only had to deal with the reactions of others, but faced the additional burden of having to explain their condition. CONCLUSION: Nurses may have little knowledge about scleroderma. It is possible that they, through their ignorance of such rare conditions, may stigmatize individuals. Through understanding about rare diseases will they be able to teach patients the skills necessary to help them cope with their symptoms, as well as the reactions of others to their diagnosis and appearance.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".