The emotional impact of the COVID-19 pandemic on individuals with progressive multiple sclerosis
Bibliographic record
Abstract
OBJECTIVE: Individuals with pre-existing chronic illness have shown increased anxiety and depression due to COVID-19. Here, we examine the impact of the COVID-19 pandemic on emotional symptomatology and quality of life in individuals with Progressive Multiple Sclerosis (PMS). METHODS: Data were obtained during a randomized clinical trial on rehabilitation taking place at 11 centers in North America and Europe. Participants included 131 individuals with PMS. Study procedures were interrupted in accordance with governmental restrictions as COVID-19 spread. During study closure, a COVID Impact Survey was administered via telephone or email to all participants, along with measures of depressive symptoms, anxiety symptoms, quality of life, and MS symptomatology that were previously administered pre-pandemic. RESULTS: 4% of respondents reported COVID-19 infection. No significant changes were noted in anxiety, quality of life, or the impact of MS symptomatology on daily life from baseline to lockdown. While total HADS-depression scores increased significantly at follow-up, this did not translate into more participants scoring above the HADS threshold for clinically significant depression. No significant relationships were noted between disease duration, processing speed ability or EDSS, and changes in symptoms of depression or anxiety. Most participants reported the impact of the virus on their psychological well-being, with a little impact on financial well-being. The perceived impact of the pandemic on physical and psychological well-being was correlated with the impact of MS symptomatology on daily life, as well as changes in depression. CONCLUSIONS: Overall, little change was noted in symptoms of depression or anxiety or overall quality of life.
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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 | 0.001 | 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 | 0.000 |
| 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".