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Record W4385801800 · doi:10.1089/derm.2023.0111

Patients' and Caregivers' Preferences for Mental Health Care and Support in Atopic Dermatitis

2023· article· en· W4385801800 on OpenAlexvenueno aff
Albert C. Chong, Alan Schwartz, Jessica Lang, Peck Y. Ong, Ian A. Myles, Jonathan I. Silverberg, Korey Capozza

Bibliographic record

VenueDermatitis · 2023
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthMedicineAtopic dermatitisAnxietyMental health careHealth carePsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.186
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.273
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2023
Admission routes1
Has abstractyes

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