Evidence map›Paper›PMID 34281723›Full record

ArticlePatient education and counseling2022

Using crowdsourced medicine to manage uncertainty on Reddit: The case of COVID-19 long-haulers.

Charee M Thompson, Kayla B Rhidenour, Kate G Blackburn, Ashley K Barrett, Sara Babu

Open access · greenAbstract read
In one paragraph

Article in Patient education and counseling, 2022. 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
11.4field-weighted citation impact, top 2% 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

15 citing papers in PubMed, 35 citations in OpenAlex.

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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

5 authors at 3 institutions in 1 country.

Charee M ThompsonDepartment of Communication, University of Illinois, Urbana-Champaign, IL, USA. Electronic address: charee@illinois.edu.
Kayla B RhidenourDepartment of Communication, Baylor University, TX, USA.
Kate G BlackburnDepartment of Psychology, University of Texas at Austin, TX, USA.
Ashley K BarrettDepartment of Communication, Baylor University, TX, USA.
Sara BabuDepartment of Communication, University of Illinois, Urbana-Champaign, IL, USA.
Baylor University · USUniversity of Illinois Urbana-Champaign · USThe University of Texas at Austin · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveCauses of and treatments for long-COVID syndrome remain unknown. Drawing on uncertainty management theory (UMT), this study elucidates the communicative nature of crowdsourced medicine as a means by which COVID "long-haulers" respond to their poorly understood illness.

methods31,892 posts on the long-haulers subreddit (r/covidlonghaulers) were analyzed, starting with its creation date, July 24th, 2020, until January 7, 2021. The Meaning Extraction Method was used to identify clusters of words that mathematically group together across the text observations.

resultsAnalyses yielded 16 distinct factors of words, which we thematized based on their composition, the data, and UMT. The 16 themes encompassed symptoms (e.g., pain, respiratory, sensory), diagnostic concerns (testing, diagnosis), broad health concerns (immunity, physical activity, diet), chronicity, support, identity, and anxiety.

conclusionFindings provide a succinct, yet robust set of themes reflecting the information-seeking (i.e., "This is happening to me") and support-seeking functions of long-haulers' talk (i.e., "Is this happening to you?"). Findings have implications for collective uncertainty management, online crowdsourcing, and patient advocacy. PRACTICE IMPLICATIONS: We recommend that health care providers employ sensitivity when addressing the anxiety that long-haulers are experiencing while also validating that their physical symptoms are real. Online communities help long-haulers manage their uncertainty.

Indexed as

COVID-19CrowdsourcingHumansPost-Acute COVID-19 SyndromeSARS-CoV-2UncertaintyCOVID-19CrowdsourcingSocial mediaUncertainty management

Identifiers

PMID34281723
PMCPMC8805953
OpenAlexW3180105200

What OpenQuestion holds

Textmetadata
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.