Unintended consequences of measures implemented in the school setting to contain the COVID-19 pandemic: a scoping review
Notice bibliographique
Résumé
BACKGROUND: With the emergence of SARS-CoV-2 in late 2019, governments worldwide implemented a multitude of non-pharmaceutical interventions in order to control the spread of the virus. Most countries have implemented measures within the school setting in order to reopen schools or keep them open whilst aiming to contain the spread of SARS-CoV-2. For informed decision-making on implementation, adaptation, or suspension of such measures, it is not only crucial to evaluate their effectiveness with regard to SARS-CoV-2 transmission, but also to assess their unintended consequences. OBJECTIVES: To comprehensively identify and map the evidence on the unintended health and societal consequences of school-based measures to prevent and control the spread of SARS-CoV-2. We aimed to generate a descriptive overview of the range of unintended (beneficial or harmful) consequences reported as well as the study designs that were employed to assess these outcomes. This review was designed to complement an existing Cochrane Review on the effectiveness of these measures by synthesising evidence on the implications of the broader system-level implications of school measures beyond their effects on SARS-CoV-2 transmission. SEARCH METHODS: We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, four non-health databases, and two COVID-19 reference collections on 26 March 2021, together with reference checking, citation searching, and Google searches. SELECTION CRITERIA: We included quantitative (including mathematical modelling), qualitative, and mixed-methods studies of any design that provided evidence on any unintended consequences of measures implemented in the school setting to contain the SARS-CoV-2 pandemic. Studies had to report on at least one unintended consequence, whether beneficial or harmful, of one or more relevant measures, as conceptualised in a logic model. DATA COLLECTION AND ANALYSIS: We screened the titles/abstracts and subsequently full texts in duplicate, with any discrepancies between review authors resolved through discussion. One review author extracted data for all included studies, with a second review author reviewing the data extraction for accuracy. The evidence was summarised narratively and graphically across four prespecified intervention categories and six prespecified categories of unintended consequences; findings were described as deriving from quantitative, qualitative, or mixed-method studies. MAIN RESULTS: Eighteen studies met our inclusion criteria. Of these, 13 used quantitative methods (3 experimental/quasi-experimental; 5 observational; 5 modelling); four used qualitative methods; and one used mixed methods. Studies looked at effects in different population groups, mainly in children and teachers. The identified interventions were assigned to four broad categories: 14 studies assessed measures to make contacts safer; four studies looked at measures to reduce contacts; six studies assessed surveillance and response measures; and one study examined multiple measures combined. Studies addressed a wide range of unintended consequences, most of them considered harmful. Eleven studies investigated educational consequences. Seven studies reported on psychosocial outcomes. Three studies each provided information on physical health and health behaviour outcomes beyond COVID-19 and environmental consequences. Two studies reported on socio-economic consequences, and no studies reported on equity and equality consequences. AUTHORS' CONCLUSIONS: We identified a heterogeneous evidence base on unintended consequences of measures implemented in the school setting to prevent and control the spread of SARS-CoV-2, and summarised the available study data narratively and graphically. Primary research better focused on specific measures and various unintended outcomes is needed to fill knowledge gaps and give a broader picture of the diverse unintended consequences of school-based measures before a more thorough evidence synthesis is warranted. The most notable lack of evidence we found was regarding psychosocial, equity, and equality outcomes. We also found a lack of research on interventions that aim to reduce the opportunity for contacts. Additionally, study investigators should provide sufficient data on contextual factors and demographics in order to ensure analyses of such are feasible, thus assisting stakeholders in making appropriate, informed decisions for their specific circumstances.
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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 | 0,100 | 0,443 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,015 | 0,002 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,003 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».