Mapping the intersection of demographics, behavior, and government response to the COVID-19 pandemic: an observational cohort study
Notice bibliographique
Résumé
BACKGROUND: During the early phase of the COVID-19 pandemic, the province of Ontario enacted restrictions and recommendations that changed over time. These measures were effective in reducing COVID-19-related illness and deaths, but adherence to these non-pharmaceutical interventions may be modified by individual factors including demographics and health status which shape exposure risk behaviors. METHODS: A total of 348 participants completed baseline questionnaires (to assess demographics, pre-pandemic exposure risk, and health status), weekly illness reports, and monthly social distancing behavior questionnaires to evaluate exposure risk over time in response to changing levels of government restrictions. Exposure risk behaviors were calculated using seven categories: attendance at social events, receiving care (hospital, etc.), visiting or volunteering at care facilities, public transportation use, hours working outside of the home, hours volunteering outside of the home, and handwashing frequency. The impact of individual and environmental factors on exposure risk over time was evaluated by a Poisson family generalized linear mixed model. RESULTS: Participants across all age groups and health statuses adapted their behaviors in response to evolving regulations, but older individuals and those with pre-existing conditions had the largest change in behavior. These individuals also had the most severe symptoms when they developed COVID-19 or other influenza-like illnesses. Participants who were older or had pre-existing health conditions had lower levels of exposure risk overall, and this was largely driven by a lower prevalence and frequency of in-person work. Female participants also had lower levels of exposure risk overall, consistent with an increased frequency of handwashing in this group. Unexpectedly, we found no effect of vaccination on total exposure risk. CONCLUSIONS: Participant behavior was generally responsive to government-imposed restrictions, with increased stringency coinciding with decreased exposure risk among participants. Demographic-associated differences in exposure risk behaviors appear to be driven by systemic factors (i.e., a return to in-person work) to a greater extent than personal choices (i.e., social gatherings). These findings emphasize the interplay between demographic factors and government interventions in shaping individual behaviors over the course of the pandemic. Understanding these dynamics is crucial for informing interventions and mitigating the impact of future pandemics.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».