Chronic health consequences of the COVID-19 pandemic on school workers: a cross-sectional post-pandemic analysis
Bibliographic record
Abstract
IMPORTANCE: The COVID-19 pandemic dramatically affected schools. However, there are insufficient data on the chronic physical and mental health consequences of the pandemic in school workers. OBJECTIVES: To determine the prevalence and the functional and mental health impact of pandemic-related chronic health symptoms among school workers towards the end of the COVID-19 pandemic. DESIGN: Cross-sectional analysis of health questionnaires and serology testing data (nucleocapsid, N antibodies) collected between January and April 2023, within a cohort of school workers. SETTING: Three large school districts (Vancouver, Richmond, Delta) in the Vancouver metropolitan area, Canada (representing 186 elementary and secondary schools in total). PARTICIPANTS: Active school staff employed in these three school districts. EXPOSURE: COVID-19 infection history by self-reported viral and/or nucleocapsid antibody testing. MAIN OUTCOMES: Self-reported, new-onset pandemic-related chronic health symptoms that started within the past year, lasting at least 3 months, after a positive viral test among those with a known infection. RESULTS: Of 1128 school staff enrolled from 185/186 (99.5%) schools, 1086 (96.3%) and 998 (88.5%) staff completed health questionnaires and serology testing, respectively. The N-seroprevalence adjusted for clustering by school and test sensitivity and specificity was 84.7% (95% Credible Interval (95% CrI): 79.2% to 91.8%) compared with 85.4% (95% CrI: 81.6% to 90.3%) in a community-matched sample of blood donors. Overall, 31.1% (95% CI: 28.4% to 34.0%) staff reported new-onset chronic symptoms. These symptoms were more frequently reported in staff with viral test-confirmed infections (38.0% (95% CI: 34.3% to 41.9%)) compared with those with positive serology who were unaware that they had COVID-19 (14.3% (95% CI: 7.6% to 23.6%); p<0.001) or those with a negative serology (18.1% (95% CI: 12.7% to 24.6%); p<0.001). New-onset chronic symptoms were also more common in women (OR=1.6 (95% CI: 1.1 to 2.4)) and staff with a pre-existing health condition (OR=1.9 (95% CI: 1.4 to 2.5)). After controlling for age, sex and comorbidities, symptoms were associated with more days absent from work during the acute SARS-CoV-2 infection (OR=1.1 (95% CI: 1.0 to 1.2)), poorer mental health (OR=2.5 (95% CI: 1.9 to 3.4)), anxiety (OR=2.1 (95% CI: 1.5 to 3.0)) and depressive symptoms (OR=2.8 (95% CI: 2.0 to 4.0)). CONCLUSIONS: The pandemic had major health impacts on school workers. To our knowledge, this study is among the first to concurrently quantify a broad range of chronic physical and mental health impacts, highlighting the need for further research and targeted health programmes to address this significant burden.
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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 | 0.001 |
| 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 | 0.001 | 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".