Factors associated with changes in employment in individuals with long COVID
Bibliographic record
Abstract
BackgroundLong Covid symptoms are known to have an impact on function, however, their effect on employment and the associated demographic and clinical factors are not well understood.ObjectiveOur objectives were (1) To compare changes in employment status between those with Long Covid and those who recovered from their Covid-19 infection; and (2) To identify demographic factors, clinical factors, and occupational skill level associated with decreased employment status in those with Long Covid.MethodsWe conducted an electronic survey (≥12 weeks post infection) with adult residents in Laval, Quebec, Canada who tested positive for Covid-19 between March 2020 and January 2022, regarding Long Covid symptoms and their functional impact. This analysis focuses on employment status: pre-covid, prior to infection, and current, and was recorded as full-time, part-time, or not working due to illness or for other reasons. Change in employment status was categorized as no decrease or decreased.ResultsAmong 2764 respondents, 15.6% (95% CI: 13.3-17.9) with Long Covid (versus 5.4% (95% CI: 4.2-6.5) who recovered) experienced a decrease in employment (p < 0.001). Clinical factors associated with a decrease in employment include having been hospitalized for Covid-19, having ≥1 comorbid condition prior to infection, >12 months since infection, moderate to severe decline in physical and psychological health, and decline in global health. Demographic factors and occupational skill level were not associated with a change in employment.ConclusionsPersons with Long Covid are more likely to experience a decrease in employment. Research is needed to determine whether rehabilitation for people with Long Covid could improve employment levels.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".