Psychosocial Impact of the COVID-19 Pandemic Among Omanis with Multiple Sclerosis: Single Tertiary Center Experience
Bibliographic record
Abstract
(1) Background: The COVID-19 pandemic presented unprecedented challenges for people with multiple sclerosis (PwMS) in Oman, necessitating targeted healthcare planning and patient support. This study aimed to investigate the impact of COVID-19 on MS management and disease course, incidence, and outcomes of COVID-19, psychosocial and mental health effects of the pandemic, and demographic and clinical predictors of the effects related to COVID-19 among Omani PwMS. (2) Methods: This cross-sectional study was conducted from January to April 2021. Adult (18–60 years) Omani PwMS completed a structured interview along with the Expanded Disability Status Scale (EDSS) and World Health Organization Well-being Index (WHO-5). Clinical data on relapses and disease-modifying therapies and adherence were analyzed. The data was statistically analyzed. (3) Results: Of 104 PwMS (73.1% female), 22.1% contracted COVID-19, with fatigue being the most reported symptom (87%). Female sex (p = 0.042), younger age (18–34 vs. 35–45 years; p = 0.014), diagnosis of COVID-19 (p = 0.037), and low current mental well-being scores (p = 0.021) predicted greater COVID-19-related effects. (4) Conclusion: These findings highlight the need to study the mental resilience of this subgroup of PwMS and provide them with targeted support during crises.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".