Public health measures to reduce the risk of SARS-CoV-2 transmission in Canada during the early days of the COVID-19 pandemic: a scoping review
Notice bibliographique
Résumé
OBJECTIVE: The main objectives of this study were to synthesise and compare pandemic preparedness strategies issued by the federal and provincial/territorial (P/T) governments in Canada and to assess whether COVID-19 public health (PH) measures were tailored towards priority populations, as defined by relevant social determinants of health. METHODS: This scoping review searched federal and P/T websites on daily COVID-19 pandemic preparedness strategies between 30 January and 30 April 2020. The PROGRESS-Plus equity-lens framework was used to define priority populations. All definitions, policies and guidelines of PH strategies implemented by the federal and P/T governments to reduce risk of SARS-CoV-2 transmission were included. PH measures were classified using a modified Public Health Agency of Canada Framework for Canadian Pandemic Influenza Preparedness. RESULTS: A total of 722 COVID-19 PH measures were issued during the study period. Of these, home quarantine (voluntary) (n=13.0%; 94/722) and retail/commerce restrictions (10.9%; n=79/722) were the most common measures introduced. Many of the PH orders, including physical distancing, cancellation of mass gatherings, school closures or retail/commerce restrictions began to be introduced after 11 March 2020. Lifting of some of the PH orders in phases to reopen the economy began in April 2020 (6.5%; n=47/722). The majority (68%, n=491/722) of COVID-19 PH announcements were deemed mandatory, while 32% (n=231/722) were recommendations. Several PH measures (28.0%, n=202/722) targeted a variety of groups at risk of socially produced health inequalities, such as age, religion, occupation and migration status. CONCLUSIONS: Most PH measures centred on limiting contact between people who were not from the same household. PH measures were evolutionary in nature, reflecting new evidence that emerged throughout the pandemic. Although ~30% of all implemented COVID-19 PH measures were tailored towards priority groups, there were still unintended consequences on these populations.
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 | 0,021 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,002 |
| 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 ».