Risk Perceptions, Knowledge and Behaviors of General and High-Risk Adult Populations towards COVID-19: A Systematic Scoping Review
Notice bibliographique
Résumé
Abstract Background The COVID-19 pandemic represents a major crisis for governments and populations around the globe. A large number of studies have been conducted worldwide to understand people’s awareness and behavioral response towards the disease. The public’s risk perceptions, knowledge, and behaviors are key factors that play a vital role in the transmission of infectious diseases. Our scoping review aims to map the early evidence on risk perceptions, knowledge, and behaviors of general and high-risk adult populations towards COVID-19. Methods A systematic scoping review was conducted of peer-reviewed articles in five databases (MEDLINE-Ovid, EMBASE-Ovid, PsycINFO-Ovid, Web of Science, and CINAHL-EBSCO) on studies conducted during the early stage of COVID-19 (January to June, 2020). The gray literature was also searched through Open Grey, Scopus, Wonder, Social Science Research Network, MedRxiv, and websites of major public health organizations. Twenty studies meeting the inclusion criteria were included, appraised and analyzed. Results During the early stage of the pandemic, levels of risk perceptions, knowledge, and behaviors towards COVID-19 were moderate to high in both general and high-risk adult populations. The perceived severity of the disease was slightly higher than the perceived susceptibility of getting COVID-19 during the first wave of COVID-19. Adults were knowledgeable about preventive behaviors, including hand-washing, mask-wearing, social distancing, and avoidance behaviors. Nevertheless, an important knowledge gap regarding the asymptomatic transmission of COVID-19 was reported in many studies. Our review identified hand-washing and avoiding crowded places as dominant preventive behaviors at the early stage of the pandemic. Staying at home, reducing social contacts, and avoiding public transport were less widespread in general populations than in high-risk adult groups. Being a female, older, and more educated was associated with better knowledge of COVID-19 and appropriate preventive behaviors. Conclusion This scoping review offers a first understanding of general and high-risk adults’ risk perceptions, knowledge, and behaviors towards COVID-19 during the early stage of the COVID-19 pandemic. Further research should be undertaken to assess psychological and behavioral responses over time. Research gaps have been identified in the relationship between ethnicity and risk perceptions, knowledge, and behaviors towards COVID-19. Contribution to the field statement Since the beginning of the pandemic, a large number of primary studies have been conducted worldwide to understand people’s awareness and behavioral response towards COVID-19. Nevertheless, no review has mapped the early evidence on the perceptions, knowledge, and preventive behaviors of adult populations towards the transmission of this new disease. To the best of our knowledge, this is the first scoping review that offers an understanding of the general and high-risk adults’ risk perceptions, knowledge, and behaviors (RPKB) towards COVID-19 during the early stage of the COVID-19 pandemic. This review also identified sociodemographic factors associated with adults’ RPKB regarding COVID-19. As the virus does not affect individuals equally, knowing these factors can help to mitigate the negative effects of COVID-19 in certain population groups by developing targeted communication strategies that will facilitate their engagement in preventive measures. Finally, research gaps have been identified in the relationship between ethnicity and RPKB towards COVID-19. The existence of a disproportionate number of COVID-19 fatalities within Black populations should signal the possible gaps in RPKB towards COVID-19 in these communities. Additional studies on ethnic health disparities can help public health authorities to introduce targeted actions towards these communities during the COVID-19 pandemic.
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 | 0,012 | 0,070 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,006 | 0,006 |
| Bibliométrie | 0,015 | 0,013 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».