Psychosocial impact of epidermolysis bullosa on patients: A qualitative study
Bibliographic record
Abstract
BACKGROUND: Epidermolysis bullosa (EB) is an inherited disorder that results in painful skin blisters requiring daily wound care. The psychosocial impact of EB is one of the most significant concerns for patients, but there is minimal research addressing these concerns or ways to improve patient quality of life (QoL). OBJECTIVE: To examine the psychosocial impact of EB on affected patients and ways to improve their QoL. METHODS: Eight EB participants were selected from the 2006 DEBRA Family Conference Day in Toronto. Participants were interviewed by a social worker and a field evaluator. The transcript of each interview was assessed using qualitative content analysis. RESULTS: Four themes were identified: school interaction, daily life, family interactions, and societal interactions. Participants reported being teased and avoided by peers, and they felt their conditions were misunderstood by the general public. School absenteeism resulted in some patients falling behind in school, which may have been misinterpreted as intellectual impairment. Patients acknowledged significant dependence on others and felt they were contributing to caregiver burden. CONCLUSIONS: Our findings highlight the psychosocial impact of EB on patients. As EB awareness and resources to support patients and caregivers have improved since this study was conducted, more studies exploring the current landscape and opportunities to improve quality of life are needed.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".