Exploring social determinants of health and their impacts on self-reported quality of life in long COVID-19 patients
Bibliographic record
Abstract
This study explores the health-related quality of life (HRQoL) experienced by patients with Long COVD-19 using data from British Columbia's post-COVID-19 Recovery Clinics. A retrospective cohort of 3463 patients was analyzed to assess HRQoL through the EQ-5D-5L questionnaire which includes five dimensions (mobility, self-care, usual activities, physical health, and mental health) administered to patients; responses were analyzed using the Visual Analogue Score (VAS). Notably, 95% of participants reported HRQoL scores below 90, with 50% scoring under 60, indicating significant impacts on their well-being. The analysis revealed that HRQoL is significantly influenced by various social determinants of health (SDoH), including age, sex, employment status, and ethnicity, each showing distinct correlations with HRQoL dimensions and overall VAS scores. Specifically, older age was associated with decreased mobility and increased pain/discomfort but less anxiety and depression, highlighting varying impacts across the age spectrum. The study highlights the multifaceted impacts of Long COVID on the lives of patients and underscores the necessity of targeted strategies to improve HRQoL among diverse groups, considering specific SDoH. Such a comprehensive approach could lead to more equitable health outcomes and support the development of tailored public health policies aimed at the recovery and rehabilitation of Long COVID sufferers.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".