Impact of Covid-19 on Mental Health and Treatment Compliance in Psoriasis Patients
Bibliographic record
Abstract
Objective: This study was designed to determine the impact of COVID-19 on treatment compliance and mental health (anxiety and depression) in psoriasis patients. Methods: A descriptive cross-sectional study was undertaken in the Department of Dermatology at York teaching Hospital from April to August 2020. One hundred and eleven patients diagnosed with psoriasis were asked to complete a questionnaire (HADS). Patients were also asked about their compliance or self-modifications in their treatment regime due to fear of Covid-19. Using SPSS version 26.0 all of the collected data was analysed together. Results: 40 patients (36.03%) had depression and 52 patients (46.84%) anxiety on the basis of their HADS score. Twenty patients (18.01%) stopped treatment due to concerns regarding COVID-19. Prevalence of anxiety was more in female patients and those on traditional immuno-suppressants as compared to biological treatment or those receiving topical treatment only. Conclusion: Depression and anxiety are common in patients with psoriasis. Female patients and those on systemic medications are worse affected compared to patients on topical treatment or those receiving phototherapy. Fear of COVID-19 has led to 18% of patients self-stopping their treatment altogether. Keywords: Psoriasis, Mental Health, Covid-19, Compliance
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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".