Evidence map›Paper›PMID 41182518›Full record

ArticleHealth care analysis : HCA : journal of health philosophy and policy2026

Layperson and Healthcare Professional Experiences of Medical Misinformation About Masks on Social Media During the Covid-19 Pandemic: A Thematic Framework Analysis.

Daniel Robinson, Steven Agius, Alistair Warren

Abstract read
In one paragraph

Article in Health care analysis : HCA : journal of health philosophy and policy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Daniel RobinsonSchool of Medicine, University of Nottingham, Nottingham, UK. dan.robinson@doctors.org.uk.
Steven AgiusSchool of Medicine, University of Nottingham, Nottingham, UK.
Alistair WarrenSchool of Medicine, University of Nottingham, Nottingham, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aimed to compare how healthcare professionals and laypeople from the UK engaged with and confronted misinformation about masks on social media and some of the barriers encountered, applying connectivism as a theoretical lens to interpret findings. Using thematic framework analysis, 12 in-depth interviews were conducted with participants consisting of seven healthcare professionals and five laypeople who actively use the social media website Twitter (now known as X). Interviews were recorded, transcribed verbatim and analysed iteratively using constant comparison and line-by-line coding. After coding of the first few interviews a working analytical framework was developed by comparing codes and grouping them together into categories. This was later applied to the subsequent interview transcripts. Analysis revealed six key categories: social capital (e.g., celebrity status), emotion (e.g., fear, anger, and burnout), culture (e.g., politics and toxic social media culture), social media dynamics (e.g., information overload), communication (e.g., managing cognitive load), and belief (e.g., beliefs being fixed and difficult to change). Our study suggests that healthcare professionals must actively engage with the public on social media by providing clear, evidence-based information, correcting false claims, and fostering trust through transparent communication. Interpreting our findings through a connectivist perspective highlights how digital networks shape trust and learning in online health discourse. Wider society, including tech companies and policymakers, should implement stronger fact-checking mechanisms, promote digital literacy, and support regulations that curb the spread of harmful health misinformation.

Indexed as

CommunicationCOVID-19Health PersonnelMasksSocial MediaDigital MediaFemaleHumansMedia ExposurePandemicsQualitative ResearchSARS-CoV-2United KingdomCovid-19Face masksMedical misinformationPublic health educationSocial media

Identifiers

PMID41182518
PMCPMC13675347

What OpenQuestion holds

Textmetadata
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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.