Chronic health consequences of the COVID-19 pandemic on school workers: a cross-sectional post-pandemic analysis
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».