Mental Fatigue, Activities of Daily Living, Sick Leave and Functional Status among Patients with Long COVID: A Cross-Sectional Study
Bibliographic record
Abstract
Studies suggest that persistent symptoms after COVID-19 (long COVID) influence functioning and activities of daily living (ADL). However, it is still uncertain how and to what extent. This study aimed to describe patient-reported mental fatigue, ADL problems, ADL ability, sick leave and functional status among patients with long COVID. In this cross-sectional study, 448 patients, ≥18 years old and referred to occupational therapy at a Danish Post-COVID-19 Clinic, were included. Mental fatigue was measured by the Mental Fatigue Scale, ADL problems and ability were measured by the Canadian Occupational Performance Measure, sick leave was self-reported and functional status was evaluated by the Post-COVID-19 Functional Status Scale. Mean age was 46.8 years, 73% of the patients were female, and 75% suffered from moderate to severe mental fatigue. The majority reported difficulties performing productive and leisure activities. The median performance and satisfaction scores were 4.8 and 3, respectively. In total, 56% of the patients were on sick leave, and 94% were referred to rehabilitation. A decrease in functional status was found between pre-COVID-19 and assessment. Conclusively, the patients were highly affected in their everyday life and had distinct rehabilitation needs. Future research is needed to address causalities and rehabilitation for this patient group.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".