Evidence map›Paper›PMID 36981512›Full record

ArticleHealthcare (Basel, Switzerland)2023

Opinion and Sentiment Analysis of Palliative Care in the Era of COVID-19.

Megumi Inoue, Meng-Hao Li, Mahdi Hashemi, Yang Yu, Jahnavi Jonnalagadda, Rajendra Kulkarni, Matthew Kestenbaum, Denise Mohess, Naoru Koizumi

Open access · goldFull text read
In one paragraph

Article in Healthcare (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
2.0field-weighted citation impact, top 14% of its field
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 7 citations in OpenAlex.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 1 country.

Megumi InoueDepartment of Social Work, George Mason University, 4400 University Drive, MS 1F8, Fairfax, VA 22030, USA.ORCID 0000-0001-8077-6711
Meng-Hao LiSchar School of Policy and Government, George Mason University, Arlington, VA 22201, USA.ORCID 0000-0003-2051-3690
Mahdi HashemiDepartment of Information Sciences and Technology, George Mason University, Fairfax, VA 22030, USA.ORCID 0000-0003-0212-0228
Yang YuSchar School of Policy and Government, George Mason University, Arlington, VA 22201, USA.
Jahnavi JonnalagaddaDepartment of Information Sciences and Technology, George Mason University, Fairfax, VA 22030, USA.
Rajendra KulkarniSchar School of Policy and Government, George Mason University, Arlington, VA 22201, USA.
Matthew KestenbaumCapital Caring Health, Falls Church, VA 22042, USA.
Denise MohessYale New Haven Health System, Bridgeport, CT 06610, USA.
Naoru KoizumiSchar School of Policy and Government, George Mason University, Arlington, VA 22201, USA.ORCID 0000-0001-8722-0898
George Mason University · USCapital Caring · USYale New Haven Health System · US

Funding

Institute for Digital Innovation (IDIA), George Mason University 215096
6 · The paper itself

Abstract

During the COVID-19 pandemic, the value of palliative care has become more evident than ever. The current study quantitatively investigated the perceptions of palliative care emerging from the pandemic experience by analyzing a total of 26,494 English Tweets collected between 1 January 2020 and 1 January 2022. Such an investigation was considered invaluable in the era of more people sharing and seeking healthcare information on social media, as well as the emerging roles of palliative care. Using a web scraping method, we reviewed 6000 randomly selected Tweets and identified four themes in the extracted Tweets: (1) Negative Impact of the Pandemic on Palliative Care; (2) Positive Impact of the Pandemic on Palliative Care; (3) Recognized Benefits of Palliative Care; (4) Myth of Palliative Care. Although a large volume of Tweets focused on the negative impact of COVID-19 on palliative care as expected, we found almost the same volume of Tweets that were focused on the positive impact of COVID-19 on palliative care. We also found a smaller volume of Tweets associated with myths about palliative care. Using these manually classified Tweets, we trained machine learning (ML) algorithms to automatically classify the remaining tweets. The automatic classification of Tweets was found to be effective in classifying the negative impact of the COVID-19.

Indexed as

COVID-19informationpalliative careTweets

Identifiers

PMID36981512
PMCPMC10048418
OpenAlexW4324141907

What OpenQuestion holds

Textfull text, public
LicenceCC BY
reference markers read1
measurements read22
Read underepoch 390

Registered trials

None linked

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.