ArticlePalliative medicine reports2024
What Does "Palliative" Mean? Sentiment, Knowledge, and Public Perception Concerning Palliative Care on the Internet since the COVID-19 Pandemic.
Article in Palliative medicine reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Can the 'Deathbed Etiquette' guide support friends and family to navigate the dying process? A qualitative study examining the views of bereaved people.Palliative care and social practice · 2026Article
- Public Perspectives on Palliative and Hospice Care: Social Media Content Analysis Using Topic Modeling and Multiclass Sentiment Analysis.Journal of medical Internet research · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Little is known about the public perception of palliative care during and after the pandemic. Assuming that analyzing online language data has the potential to collect real-time public opinions, an analysis of large online datasets can be beneficial to guide future policymaking. Objectives: To identify long-term effects of the COVID-19 pandemic on the public perception of palliative care and palliative care-related misconceptions on the Internet (worldwide) through natural language processing (NLP). Design: Using large language model NLP analysis, we identified public attitudes, opinions, sentiment, and misconceptions about palliative care on the Internet, comparing a corpus of English-language web texts and X-posts ("tweets") (02/2020-02/2022) with similar samples before (02/2018-02/2020) and after the pandemic (03/2022-02/2024). Setting: The study is a statistical analysis of website and social media data, conducted on six large language corpora. Results: Since the COVID-19 pandemic, palliative care situations are more often portrayed as frightening, uncertain, and stressful, misconceptions about the activities and aims of palliative care occur on average 44% more frequently, especially on the social media platform X. Conclusions: The impact of the COVID-19 pandemic on public discussion on social media continues to persist even in 2024. Insights from online NLP analysis helped to determine the image of palliative care in the Internet discourse and can help find ways to react to certain trends such as the spread of negative attitudes and misconceptions.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.