Patients' and Caregivers' Preferences for Mental Health Care and Support in Atopic Dermatitis
Bibliographic record
Abstract
Abstract: Background: Atopic dermatitis (AD) has large mental health impacts for patients and caregivers, yet their preferences regarding how to relieve these impacts are poorly understood. Objective: To understand patients' and caregivers' preferences for AD-related mental health care and support. Methods: We surveyed 279 adult AD patients and 154 caregivers of children with AD across 26 countries regarding their AD-related mental health burden, preferred strategies for improving AD-related mental health, and experiences with mental health care in AD. Results: Caregivers reported significantly worse overall mental health ( P = 0.01) and anxiety ( P = 0.03) than adult patients when controlling for AD severity. Among adult patients, 58% selected treating the AD, 51% managing itch, 44% wearing clothing to cover up skin, 43% avoiding social situations, and 41% spending time alone, as strategies they felt would improve their own AD-related mental health. Caregivers selected managing itch and treating the AD most frequently for both their own (76% and 75%, respectively) and their children's (75% and 61%) mental health. Adult patients were less satisfied with mental health care from mental health providers versus nonmental health providers ( P < 0.001). Conclusions: Effective AD management is the preferred method for improving mental health among patients as well as caregivers, who may experience the greatest mental health impacts. Self-care strategies should be considered in a shared decision-making 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".