The role of nurses in the management of atopic dermatitis: Results of an international survey
Bibliographic record
Abstract
Abstract Background Atopic dermatitis (AD) is a chronic inflammatory skin condition that has a major impact on the quality of life of patients and their families. Therapeutic patient education (TPE) is recommended for all patients with this condition to improve disease management, adherence to therapy, and quality of life. Nurse‐led consultation, when performed by expert nurses, can be as effective as consultation by dermatologists in this field. Objective This study aimed to examine the role of nurses in the management of patients with atopic dermatitis. Methods A global survey was carried out to determine the current role of nurses in TPE for patients with AD. A 24‐item online questionnaire was sent to all members of the ISAD‐OPENED network. Results In total, 85 health care professionals from 20 countries answered the questionnaire. About 75% of the responding physicians and nurses reported effective interprofessional collaboration. The collaboration between nurse and physician was deemed to be excellent by 51% of the respondents. According to caregivers, a trained nurse could be helpful in many other fields, such as nurse consultations, patient care demonstrations, telephone follow‐ups between consultations, and patient workshops. The main obstacles preventing nurse involvement in TPE are insufficient training of nurses, lack of recognition, and lack of specific funding. Conclusion This study emphasises the potential of the help provided by trained nurses throughout the TPE process. Doctor/nurse teams are very promising for TPE in the field of AD because of their potential to improve patient 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 | 0.003 | 0.004 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".