Perceived Impact of COVID-19 in an Underserved Community: A Natural Language Processing Approach.

Abstract

To utilise natural language processing (NLP) to analyse interviews about the impact of COVID-19 in underserved communities and to compare it to traditional thematic analysis in a small subset of interviews. NLP and thematic analysis were used together to comprehensively examine the interview data. Fifty transcribed interviews with purposively sampled adults living in underserved communities in the United States, conducted from June 2021 to May 2022, were analysed to explore the impact of the COVID-19 pandemic on social activities, mental and emotional stress and physical and spiritual well-being. NLP includes several stages: data extraction, preprocessing, processing using word embeddings and topic modelling and visualisation. This was compared to thematic analysis in a random sample of 10 interviews. Six themes emerged from thematic analysis: The New Normal, Juxtaposition of Emotions, Ripple Effects on Health, Brutal yet Elusive Reality, Evolving Connections and Journey of Spirituality and Self-Realisation. With NLP, four clusters of similar context words for each approach were analysed visually and numerically. The frequency-based word embedding approach was most interpretable and well aligned with the thematic analysis. The NLP results complemented the thematic analysis and offered new insights regarding the passage of time, the interconnectedness of impacts and the semantic connections among words. This research highlights the interdependence of pandemic impacts, simultaneously positive and negative effects and deeply individual COVID-19 experiences in underserved communities. The iterative integration of NLP and thematic analysis was efficient and effective, facilitating the analysis of many transcripts and expanding nursing research methodology. While thematic analysis provided richer, more detailed themes, NLP captured new elements and combinations of words, making it a promising tool in qualitative analysis. Not applicable. No patient or public contribution.