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Application of Public Health Precautionary Guidelines Before Contracting COVID-19: Lessons Learned from a Qualitative Descriptive Study

BACKGROUND: The COVID-19 pandemic has affected the Saudi population despite early efforts taken by officials. This study aimed to describe the precautionary measures taken by patients prior to their diagnosis with COVID-19. METHODS: A qualitative descriptive design was employed to collect data from...

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Detalles Bibliográficos
Autores principales: Almulla, Hebah, Al-Ansari, Asim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9901455/
https://www.ncbi.nlm.nih.gov/pubmed/36755749
http://dx.doi.org/10.2147/RMHP.S396153
Descripción
Sumario:BACKGROUND: The COVID-19 pandemic has affected the Saudi population despite early efforts taken by officials. This study aimed to describe the precautionary measures taken by patients prior to their diagnosis with COVID-19. METHODS: A qualitative descriptive design was employed to collect data from a convenience sample of 26 participants who had COVID-19 and recovered. Data was collected through conducting telephone interviews and was then transcribed and analyzed using inductive content analysis methods adapted from grounded theory. RESULTS: Data analysis yielded six categories of precautionary measures participants reported using to minimize their risk of contracting COVID-19: (1) wearing a mask, (2) staying at home and avoiding crowds, (3) washing hands and wearing gloves, (4) following social distancing, (5) using sanitizers, and (6) not interacting with anyone. CONCLUSION: The discrepancy found between the participants’ reported precautionary measures and their actual behavior, as well as the consistency and specificity with which they implemented some and not all measures raise concerns about existing public health messages. The study findings support the importance of well-structured and detailed communication when designing future public health campaigns and data-driven policies.