Impact of the COVID‐19 Pandemic on the Employment of Canadian Young Adults With Rheumatic Disease: Findings From a Longitudinal Survey
Bibliographic record
Abstract
OBJECTIVE: The COVID-19 pandemic has had considerable economic repercussions for young workers. The current study was undertaken to examine the impact of the pandemic on the employment of young adults with rheumatic disease and on perceptions of work and health. METHODS: Surveys were administered to young adults with rheumatic disease prior to and following the onset of the COVID-19 pandemic. Surveys asked about employment status and collected information on sociodemographic, disease/health, and work-context factors. Items also asked about the perceived impact of the COVID-19 pandemic on work and health. A generalized estimating equation model was fitted to examine the effect of the pandemic on employment. RESULTS: In total, 133 young adults completed the pre-COVID-19 pandemic survey (mean age 28.9 years, 82% women). When compared to the pre-COVID-19 pandemic period, employment decreased from 86% to 71% following the pandemic, but no other changes were identified in sociodemographic, disease/health, or work-context factors. The time period following the COVID-19 pandemic was associated with a 72% lower odds of employment compared to the pre-pandemic period (odds ratio 0.28 [95% confidence interval 0.11-0.71]). Those with a postsecondary education or who reported more mental job demands were more likely to be employed following the onset of the pandemic. Also, a majority of participants reported that the pandemic affected health care (83%), treatment access (54%), working conditions (92%), and occupational health and safety (74%). CONCLUSION: The onset of the COVID-19 pandemic had socioeconomic implications for young people with rheumatic disease. To support economic recovery for individuals with rheumatic disease, strategies to promote employment should be designed that account for the young adult life phase and occupational characteristics.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".