Long-Term Health-Related Quality of Life in Working-Age COVID-19 Survivors: A Cross-Sectional Study
Bibliographic record
Abstract
BACKGROUND: Most working-age (18-64) adults have been infected with SARS-CoV-2, and some may have developed post-COVID-19 condition (PCC). However, long-term health-related quality of life (HRQOL) following infection remains uncharacterized. METHODS: In this cross-sectional study, COVID-19 survivors from throughout British Columbia (BC), Canada, completed a questionnaire >2 years after infection. PCC status was self-reported, and HRQOL was assessed using the EuroQol 5-dimension 5-level (EQ-5D-5L) instrument. We compared HRQOL in those with current PCC, those with recovered PCC, and those without a history of PCC. Multivariable analyses were weighted to be representative of COVID-19 survivors in BC. RESULTS: Of the 1,135 analyzed participants, 19.2% had current PCC, and 27.6% had recovered PCC. Compared to those without a history of PCC, participants with recovered PCC had a similar mean EQ-5D health utility (adjusted difference -0.02 [95%CI -0.03, 0.00]), but those with current PCC had a lower health utility (adjusted difference -0.08 [95%CI -0.12, -0.05]). Participants with current PCC were also more likely to report problems with mobility (adjusted odds ratio (aOR) 6.00 [95%CI 2.88-12.52]), self-care (aOR 5.96 [95%CI 1.84-19.32]), usual activities (aOR 8.00 [95%CI 4.27-14.99]), pain/discomfort (aOR 4.28 [95%CI 2.46-7.48]), and anxiety/depression (aOR 3.45 [95%CI 1.90-6.27]). CONCLUSIONS: In working-age adults who have survived >2 years following COVID-19, HRQOL is high among those who never had PCC or have recovered from PCC. However, individuals with ongoing symptoms have lower HRQOL and are more likely to have functional deficits. These findings underscore the importance of implementing targeted healthcare interventions to improve HRQOL in adults with long-term PCC.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".