Why antimicrobial resistance messaging fails: qualitative insights interpreted through the elaboration likelihood model Article Swipe
Eva M. Krockow
,
David R. Jenkins
,
Samkele Mkumbuzi
,
Stephen J. Flusberg
,
Carolyn Tarrant
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/jacamr/dlaf148
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/jacamr/dlaf148
Background Antimicrobial resistance (AMR) is a global threat, yet public awareness remains low. This study examined perceptions of current AMR communications to improve knowledge, extending previous research through qualitative data analysed using the Elaboration Likelihood Model (ELM). Methods We held 3 focus groups (n = 15) with UK patients with recent experience of AMR and 4 (n = 14) with hospital doctors experienced in AMR treatment and communication. Semi-structured questions explored perceptions of public AMR messaging. Data were analysed using reflexive thematic analysis. Results Most participants found public AMR information difficult to access, overly technical, and unclear. They struggled to find personal and cultural relevance, described the tone as punitive and highlighted contradictory advice (e.g. discouraging antibiotic use while recommending full course completion), undermining argument quality. Some appreciated buzzwords like ‘superbugs’, but most felt that messages lacked impact and ‘punch’. When viewed through the ELM, the problematic tone and lack of personalisation reduced recipients’ motivation. The lack of readily available, clear information hindered their ability to engage deeply with messages via ‘central route’ processing, reducing the likelihood of elaboration and subsequent persuasion. Attitude change from peripheral route information processing was equally questionable given the lack of persuasive message cues. Conclusions Current AMR messaging is insufficient and communication theory could highlight areas for improvement. Our ELM analysis suggests a need to enhance motivation, capability, and argument quality while adding persuasive, peripheral cues. Personally and culturally tailored messages with a positive, solution-focused tone and simplified, engaging language may boost impact and promote lasting attitude change.
Related Topics
Concepts
Persuasion
Elaboration likelihood model
Thematic analysis
Resistance (ecology)
Argument (complex analysis)
Psychology
Quality (philosophy)
Focus group
Relevance (law)
Qualitative research
Social psychology
Medicine
Political science
Business
Sociology
Marketing
Law
Ecology
Philosophy
Epistemology
Social science
Biology
Internal medicine
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/jacamr/dlaf148
- OA Status
- gold
- Cited By
- 1
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413105598
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413105598Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/jacamr/dlaf148Digital Object Identifier
- Title
-
Why antimicrobial resistance messaging fails: qualitative insights interpreted through the elaboration likelihood modelWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-02Full publication date if available
- Authors
-
Eva M. Krockow, David R. Jenkins, Samkele Mkumbuzi, Stephen J. Flusberg, Carolyn TarrantList of authors in order
- Landing page
-
https://doi.org/10.1093/jacamr/dlaf148Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1093/jacamr/dlaf148Direct OA link when available
- Concepts
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Persuasion, Elaboration likelihood model, Thematic analysis, Resistance (ecology), Argument (complex analysis), Psychology, Quality (philosophy), Focus group, Relevance (law), Qualitative research, Social psychology, Medicine, Political science, Business, Sociology, Marketing, Law, Ecology, Philosophy, Epistemology, Social science, Biology, Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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40Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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