Evidence map›Paper›PMID 41000332›Full record

ArticleFrontiers in digital health2025

Examining the use of different message categories to communicate AMR: a content analysis of instagram posts.

Sana Parveen, Wei-Jan Chang, Patricia McHugh, Akke Vellinga

Abstract read
In one paragraph

Article in Frontiers in digital health, 2025. 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
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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

4 authors.

Sana ParveenCARA Network, School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland.
Wei-Jan ChangCARA Network, School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland.
Patricia McHughJ.E. Cairnes School of Business & Economics, University of Galway, Galway, Ireland.
Akke VellingaCARA Network, School of Public Health, Physiotherapy and Sports Science, University College Dublin, Dublin, Ireland.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background/objectives: Antimicrobial resistance (AMR) is a major worldwide concern with severe implications for public health, contributing to almost 5 million deaths in 2019. One of the main causes of AMR is overuse and misuse of antibiotics, which can be addressed by increasing awareness and educating the public on this issue. Studies have demonstrated the potential of social media to educate the public and influence behaviour. Instagram's unique features, such as its visual nature and user-friendly interface, make it well-suited for exploring health behaviours and disseminating information on various health topics. Research shows 65.4% of young adults (18-36 years old) use Instagram as their main source of information. Methods: This study examined AMR posts from Instagram from January 1, 2017 to July 15, 2023. In total, 4,606 images and videos were initially extracted which corresponded to 3,261 Instagram posts. After data cleaning, a final dataset of 574 posts were categorised into 6 message categories which were humour, shock/disgust/fear, personal stories/statements, educational/informative, opportunistic and advocacy. Results: The most common post category was educational/informative (78%) and humour was the least common (2%). We also looked at the average engagement (likes) with these posts, the educational/informative category received the most likes per post (mean of 30). The fear/shock/disgust category received 25 likes per post, humour and personal stories/statements 18 and 21 respectively. Conclusions: Our study shows Instagram has hardly been used for AMR interventions. An important population group of young adults who use Instagram as their main source of information, is missed in public health messaging on AMR.

Indexed as

AMRpublic health campaignspublic health communicationpublic health messagingsocial marketingsocial media

Identifiers

PMID41000332
PMCPMC12457341

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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.