P.013 Psychosocial Impact of COVID-19 pandemic among Omanis with Multiple Sclerosis: a single tertiary center experience
Bibliographic record
Abstract
Background: The COVID-19 pandemic posed significant challenges for people with multiple sclerosis (PwMS) in Oman, including heightened stress, treatment disruptions, and risks associated with immunosuppressive therapies. This study aimed to evaluate the pandemic’s impact on MS management, COVID-19 incidence and outcomes, psychosocial and mental health effects, and demographic and clinical predictors influencing these outcomes among Omani PwMS. Methods: In this cross-sectional study conducted from January to April 2021, 104 PwMS aged 18–60 participated in structured interviews and completed the Expanded Disability Status Scale (EDSS) and the World Health Organization Well-being Index (WHO-5). Clinical data on relapse rates, disease-modifying therapies (DMTs), and treatment adherence were analyzed using descriptive and inferential statistics. Results: Of the participants, 76 (73.1%) were female, and 23 (22.1%) reported contracting COVID-19, with fatigue being the most common symptom (87%). Female sex (p = 0.042), younger age (18–34 vs. 35–45 years; p = 0.014), COVID-19 diagnosis (p = 0.037), and lower mental well-being scores (p = 0.021) were strongly associated with COVID-19-related effects. Conclusions: Key predictors of self-reported COVID-19 effects in Omani PwMS were a confirmed diagnosis, female sex, younger age, and lower mental well-being. These findings highlight the need for exploration of mental resilience in this group and interventions 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 | 0.000 |
| 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) | 0.006 | 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".