Identifying individual psychosocial and adherence support needs in patients with psoriasis: a multinational two‐stage qualitative and quantitative study
Bibliographic record
Abstract
BACKGROUND: Psoriasis has a serious impact on patients' lives. However, adherence to medications is often poor, potentially compounding the burden of disease. Identifying patients who need support with psychosocial problems, or issues with adherence, can be complex. OBJECTIVES: We aimed to develop statements that could assist the consultation process, identifying the relative importance of factors related to effective management of psoriasis for patients. METHODS: A two-stage study design was used to comprehensively identify, and assess validity of, statements describing psoriasis impact and management issues. Both components were conducted in Canada, France, Germany, Italy, Spain, the United Kingdom and the United States. Findings from patient observation and interviews were analysed for pattern strength, and were then used to inform the development of statements that were quantitatively assessed using a survey. The association of drivers towards agreement with 'my psoriasis dictates how I lead my life' was assessed using anova. RESULTS: Fifty-six patients participated in the qualitative component, and 1,884 patients using prescription medications completed the survey. Two thematic categories were identified; disappointment with treatments, and confusion regarding psoriasis associated with a lack of direction. When assessed quantitatively, key statements associated with a strong burden of psoriasis on patients' lives were related to isolation, social stigma, visible symptoms, impact on activities and feelings of hopelessness. A mixture of patient-, doctor- and treatment-related factors were among the most common reasons for non-adherence. CONCLUSION: Questioning using the statements most associated with psychosocial impact and non-adherence could help identify patients with additional support needs, and assist in overcoming adherence issues.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".