Disease-Related Internet Use and its Relevance to the Patient–Physician Relationship in Atopic Dermatitis: A Cross-Sectional Study in Germany
Bibliographic record
Abstract
Abstract: Background: Health-related internet use presents both opportunities and challenges for patients and physicians and requires a comprehensive understanding to improve individual health care in atopic dermatitis (AD). Objective: To explore differences between regular and irregular disease-related internet users, reasons for disease-related internet use, and its relevance to the patient-physician relationship in AD. Methods: This cross-sectional study recruited 221 adults with AD online and from a German university clinic between August 2021 and February 2022. The questionnaire queried sociodemographic and disease-related information, reasons for and against using the internet, types of channels used, and the impact on the patient-physician relationship. Participants were categorized as regular (≥once per month) and irregular ( Results: Regular disease-related internet use was prevalent (n = 122/221, 55.2%) and associated with self-assessed disease severity ( P = 0.019). “Moderate” (odds ratio [OR]: 2.37, confidence interval [CI]: 1.16–4.86) and “severe” (OR: 2.84, CI: 1.12–7.19) psychological distress and general physician treatment (OR: 4.10, CI: 1.85–9.09) were the strongest predictors of regular use. Most respondents researched information to address gaps in physician-provided information (n = 107/173, 61.8%) or desired physician-recommended disease-related websites (n = 94/173, 54.3%). One-quarter (n = 39/172, 22.7%) indicated that discussing online health information with physicians strained the relationship. Conclusions: This study highlights the pivotal role of the internet for individuals affected by AD, warranting its consideration in patient education and access to health care. Physicians should provide information on suitable online resources and address concerns about discussing online activities to promote patient-centered health care.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".