| Literature DB >> 33207740 |
Huseyin Arasli1, Trude Furunes1, Kaveh Jafari2, Mehmet Bahri Saydam2, Zehra Degirmencioglu3.
Abstract
The world has been affected by an outbreak of the novel coronavirus (COVID-19). Health care workers are among those most at risk of contracting the virus. In the fight against the coronavirus, nurses play a critical role. Still, most social media platforms demonstrate that nurses fear that their health is not being prioritized. The purpose of this study is to investigate nurses' experiences through analyzing the main themes shared on Instagram by nurses during the COVID-19 pandemic. In contrast with highly structured research, the current paper highlights nurses' natural language use in describing their experiences during the first months of the outbreak in their workplace. Instagram captions were utilized as a data source. Leximancer was utilized for the content analysis of nurses' narratives towards their coronavirus experience. We sought to accomplish three research objectives: the first was to identify the main themes in the descriptions of nurses' experiences shared via their social media, specifically Instagram; then, to determine the relationships among concepts, and finally, to give useful implications based on the findings. The current study uses a qualitative (i.e., narratives) approach to analyze the main components of the nurses' experiences during the pandemic. The Leximancer software analysis revealed nine major textual themes and the relationships among these themes. In order of the relative importance, the themes were "patients", "coronavirus", "exhaustion", "family", "hospital", "personal protective equipment" (PPE), "shift", "fear", and "uncertainty". The results offer practical implications based on the social media information regarding nurses' overall experiences.Entities:
Keywords: COVID-19; Instagram; content analysis; nurses; nurses’ perception; social media
Mesh:
Year: 2020 PMID: 33207740 PMCID: PMC7696738 DOI: 10.3390/ijerph17228484
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 3.390
Figure 1An example of a user caption extracted from Instagram used in the research.
Figure 2Project stages of the Leximancer software.
Figure 3The basic model of the semantic configuration extraction in Leximancer. Source: adopted from Crofts and Bisman (2010).
Figure 4Concept map.
Main themes, concepts, and relevancy percentages.
| Themes | Concepts | Relevancy |
|---|---|---|
| Patients | patient | 100% |
| care | 44% | |
| risk | 41% | |
| support | 33% | |
| donation | 27% | |
| necessary | 38% | |
| Coronavirus | coronavirus | 37% |
| motivation | 31% | |
| proud | 28% | |
| fight | 19% | |
| Exhaustion | exhaustion | 39% |
| marks | 30% | |
| life | 21% | |
| Family | family | 20% |
| patients | 18% | |
| challenging | 18% | |
| risk | 14% | |
| Hospital | hospital | 18% |
| team | 11% | |
| Shift | shift | 15% |
| night | 9% | |
| Uncertainty | uncertainty | 11% |
| sleep | 9% | |
| Fear | fear | 12% |
| Personal protective equipment | Personal protective equipment | 12% |