Evidence map›Paper›PMID 34469327›Full record

ArticleJournal of medical Internet research2021

Using Twitter Comments to Understand People's Experiences of UK Health Care During the COVID-19 Pandemic: Thematic and Sentiment Analysis.

Esther Ainley, Cara Witwicki, Amy Tallett, Chris Graham

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
–field-weighted citation impact
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

15 citing papers in PubMed.

  1. Article
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  6. Review
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  9. Mining social media data to inform public health policies: a sentiment analysis case study.Revista panamericana de salud publica = Pan American journal of public health · 2024
    Article
  10. Article
  11. Article
  12. Sentiment and emotion trends in nurses' tweets about the COVID-19 pandemic.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2022
    Article
  13. Article
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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

4 authors.

Esther AinleyPicker Institute Europe, Oxford, United Kingdom.ORCID 0000-0001-7188-4681
Cara WitwickiPicker Institute Europe, Oxford, United Kingdom.ORCID 0000-0003-3285-8514
Amy TallettPicker Institute Europe, Oxford, United Kingdom.ORCID 0000-0002-3386-0731
Chris GrahamPicker Institute Europe, Oxford, United Kingdom.ORCID 0000-0002-6410-8171

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe COVID-19 pandemic has led to changes in health service utilization patterns and a rapid rise in care being delivered remotely. However, there has been little published research examining patients' experiences of accessing remote consultations since COVID-19. Such research is important as remote methods for delivering some care may be maintained in the future.

objectiveThe aim of this study was to use content from Twitter to understand discourse around health and care delivery in the United Kingdom as a result of COVID-19, focusing on Twitter users' views on and attitudes toward care being delivered remotely.

methodsTweets posted from the United Kingdom between January 2018 and October 2020 were extracted using the Twitter application programming interface. A total of 1408 tweets across three search terms were extracted into Excel; 161 tweets were removed following deduplication and 610 were identified as irrelevant to the research question. The remaining relevant tweets (N=637) were coded into categories using NVivo software, and assigned a positive, neutral, or negative sentiment. To examine views of remote care over time, the coded data were imported back into Excel so that each tweet was associated with both a theme and sentiment.

resultsThe volume of tweets on remote care delivery increased markedly following the COVID-19 outbreak. Five main themes were identified in the tweets: access to remote care (n=267), quality of remote care (n=130), anticipation of remote care (n=39), online booking and asynchronous communication (n=85), and publicizing changes to services or care delivery (n=160). Mixed public attitudes and experiences to the changes in service delivery were found. The proportion of positive tweets regarding access to, and quality of, remote care was higher in the immediate period following the COVID-19 outbreak (March-May 2020) when compared to the time before COVID-19 onset and the time when restrictions from the first lockdown eased (June-October 2020).

conclusionsUsing Twitter data to address our research questions proved beneficial for providing rapid access to Twitter users' attitudes to remote care delivery at a time when it would have been difficult to conduct primary research due to COVID-19. This approach allowed us to examine the discourse on remote care over a relatively long period and to explore shifting attitudes of Twitter users at a time of rapid changes in care delivery. The mixed attitudes toward remote care highlight the importance for patients to have a choice over the type of consultation that best suits their needs, and to ensure that the increased use of technology for delivering care does not become a barrier for some. The finding that overall sentiment about remote care was more positive in the early stages of the pandemic but has since declined emphasizes the need for a continued examination of people's preference, particularly if remote appointments are likely to remain central to health care delivery.

Indexed as

COVID-19Social MediaCommunicable Disease ControlDelivery of Health CareHumansPandemicsSARS-CoV-2United KingdomCOVID-19digital healthpatient experiencephone consultationpublic healthpublic opinionremote health caresentiment analysissocial mediaTwittervideo consultation

Identifiers

PMID34469327
PMCPMC8547412

What OpenQuestion holds

Textmetadata
LicenceCC BY
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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.