Healthcare Providers Face Numerous Challenges in Treating Patients with Psoriasis: Results from a Mixed-Methods Study
Bibliographic record
Abstract
Background: The paradigm shift toward biologic medications in psoriasis care requires healthcare providers (HCPs) to become acquainted with mechanisms of action and safety profiles of these new treatments to confidently use them in practice. A better understanding of this paradigm shift is necessary to provide adequate education for HCPs in psoriasis care. Objectives: This study assessed clinical practice gaps and challenges experienced by HCPs caring for patients with psoriasis. Methods: A mixed-methods approach was used to identify practice gaps and clinical challenges of dermatologists, rheumatologists, primary care physicians, physician assistants, and nurse practitioners with various levels of clinical experience in academic and community-based settings. Qualitative and quantitative data were collected sequentially. Interviews were transcribed and thematically analyzed. Results: A total of 380 psoriasis care providers in Canada and the US participated in this study. Analysis revealed challenges in establishing an accurate diagnosis of psoriasis (including screening for sub-type and distinguishing psoriasis from other skin conditions), selecting treatment (particularly regarding recently approved treatments), monitoring side effects, and collaborating with other HCPs involved in psoriasis care. Conclusion: These findings highlight educational needs of HCPs involved in psoriasis care that could have repercussions on accurate and timely diagnosis of the condition, treatment initiation, side effect monitoring, and continuity of care. Findings provide a starting point for clinicians to reflect on their practice and for the improvement of continuing professional development interventions that would bridge these gaps.
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 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 | 0.047 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".