Characterising disability in patients with long COVID–does gender matter? an analytic cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Long COVID affects patients' daily functioning and activity participation. However, gender-specific differences remain insufficiently explored despite well-documented effects of gender on health outcomes. This study examines gender differences in sociodemographic characteristics, employment status, sick leave, and mental fatigue among individuals with long COVID. Furthermore, this study explores self-reported and prioritised problems with activities of daily living (ADL) across genders and compares patterns between women and men. We hypothesised that traditional gender roles would manifest in distinct challenges for women and men. METHODS: We included 780 individuals (567 women and 213 men) diagnosed with long COVID who were referred to occupational therapy at a Danish outpatient clinic for long COVID. Sociodemographic characteristics, employment status, and sick leave were self-reported. Mental fatigue was assessed using the Mental Fatigue Scale, and ADL problems using the Canadian Occupational Performance Measure. A qualitative deductive content analysis was conducted to further categorise prioritised ADL problems. RESULTS: A higher proportion of women than men had higher education (55% vs. 37%). No statistically significant gender difference was seen in the prevalence of sick leave (57% vs. 50%). Moderate to severe mental fatigue was reported by 78% of women and 68% of men, with women reporting significantly higher fatigue (p < 0.001). Minor gender differences in ADL problems were observed, with more women reporting difficulties in household management, quiet recreation, and social interaction. Both women and men prioritised similar ADL problems, such as paid work, physical activity, social interaction, and fulfilling caregiving roles. CONCLUSION: ADL challenges reported by women and men were largely similar and had a significant impact on their daily lives. Identifying key activity limitations is essential to inform effective rehabilitation strategies.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".