Evidence map›Paper›PMID 29804314›Full record

ReviewBritish journal of health psychology2018

What are the 'active ingredients' of interventions targeting the public's engagement with antimicrobial resistance and how might they work?

Joanna L McParland, Lynn Williams, Lucyna Gozdzielewska, Mairi Young, Fraser Smith, Jennifer MacDonald, Darren Langdridge, Mark Davis, Lesley Price, Paul Flowers

Abstract readReview
In one paragraph

Review in British journal of health psychology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
25citing papers in PubMed, 4 pooled it
–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

25 citing papers in PubMed, 4 syntheses or guidelines pooled it.

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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

10 authors.

Joanna L McParlandInstitute for Applied Health, Glasgow Caledonian University, UK.ORCID 0000-0003-0580-2179
Lynn WilliamsSchool of Psychological Sciences and Health, University of Strathclyde, Glasgow, UK.
Lucyna GozdzielewskaInstitute for Applied Health, Glasgow Caledonian University, UK.
Mairi YoungInstitute for Applied Health, Glasgow Caledonian University, UK.
Fraser SmithInstitute for Applied Health, Glasgow Caledonian University, UK.
Jennifer MacDonaldInstitute for Applied Health, Glasgow Caledonian University, UK.
Darren LangdridgeFaculty of Arts and Social Sciences, The Open University, Milton Keynes, UK.
Mark DavisSchool of Sciences, Monash University, Melbourne, Victoria, Australia.
Lesley PriceInstitute for Applied Health, Glasgow Caledonian University, UK.
Paul FlowersInstitute for Applied Health, Glasgow Caledonian University, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesChanging public awareness of antimicrobial resistance (AMR) represents a global public health priority. A systematic review of interventions that targeted public AMR awareness and associated behaviour was previously conducted. Here, we focus on identifying the active content of these interventions and explore potential mechanisms of action.

methodsThe project took a novel approach to intervention mapping utilizing the following steps: (1) an exploration of explicit and tacit theory and theoretical constructs within the interventions using the Theoretical Domains Framework (TDFv2), (2) retrospective coding of behaviour change techniques (BCTs) using the BCT Taxonomy v1, and (3) an investigation of coherent links between the TDF domains and BCTs across the interventions.

resultsOf 20 studies included, only four reported an explicit theoretical basis to their intervention. However, TDF analysis revealed that nine of the 14 TDF domains were utilized, most commonly 'Knowledge' and 'Environmental context and resources'. The BCT analysis showed that all interventions contained at least one BCT, and 14 of 93 (15%) BCTs were coded, most commonly 'Information about health consequences', 'Credible source', and 'Instruction on how to perform the behaviour'.

conclusionsWe identified nine relevant TDF domains and 14 BCTs used in these interventions. Only 15% of BCTs have been applied in AMR interventions thus providing a clear opportunity for the development of novel interventions in this context. This methodological approach provides a useful way of retrospectively mapping theoretical constructs and BCTs when reviewing studies that provide limited information on theory and intervention content. Statement of contribution What is already known on this subject? Evidence of the effectiveness of interventions that target the public to engage them with AMR is mixed; the public continue to show poor knowledge and misperceptions of AMR. Little is known about the common, active ingredients of AMR interventions targeting the public and information on explicit theoretical content is sparse. Information on the components of AMR public health interventions is urgently needed to enable the design of effective interventions to engage the public with AMR stewardship behaviour. What does this study add? The analysis shows very few studies reported any explicit theoretical basis to the interventions they described. Many interventions share common components, including core mechanisms of action and behaviour change techniques. The analysis suggests components of future interventions to engage the public with AMR.

Indexed as

Drug Resistance, BacterialHealth BehaviorHealth Knowledge, Attitudes, PracticeHealth CommunicationHealth PromotionHumansMaleRetrospective Studiesantimicrobial resistancebehaviour change techniquescomponents of public health interventionsTheoretical Domains Framework

Identifiers

PMID29804314
PMCPMC6175406

What OpenQuestion holds

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LicenceCC BY-NC
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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.