Evidence map›Paper›PMID 24006959›Full record

ArticleImplementation science : IS2013

Patchy 'coherence': using normalization process theory to evaluate a multi-faceted shared decision making implementation program (MAGIC).

Amy Lloyd, Natalie Joseph-Williams, Adrian Edwards, Andrew Rix, Glyn Elwyn

Registry-linked trialAbstract read
In one paragraph

Article in Implementation science : IS, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03023813 (Individualizing Disease Prevention for Middle-Aged Adults), which is not on this map. Cited by 85 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
85citing papers in PubMed, 3 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.

NCT03023813 naterminatednot on this mapstarted 2017, after this paper: background citation

Individualizing Disease Prevention for Middle-Aged Adults

TypeinterventionalSponsorGlen TakslerRan2017 to 2021Enrolled104ConditionsPrimary Health CareArmsIntervention
3 · Its place in the literature

Who cites it

85 citing papers in PubMed, 3 syntheses or guidelines pooled it.

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  13. Patient Decision Aids into Routine Orthopedic Care: Results from an Implementation Study at 12 Sites.Medical decision making : an international journal of the Society for Medical Decision Making · 2026
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25 more citing papers are in PubMed but not listed here.

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.

Amy LloydInstitute of Primary Care and Public Health, Cardiff University School of Medicine, Heath Park, Cardiff, CF, 14 4YS, UK. glynelwyn@gmail.com.
Natalie Joseph-Williams
Adrian Edwards
Andrew Rix
Glyn Elwyn

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundImplementing shared decision making into routine practice is proving difficult, despite considerable interest from policy-makers, and is far more complex than merely making decision support interventions available to patients. Few have reported successful implementation beyond research studies. MAking Good Decisions In Collaboration (MAGIC) is a multi-faceted implementation program, commissioned by The Health Foundation (UK), to examine how best to put shared decision making into routine practice. In this paper, we investigate healthcare professionals' perspectives on implementing shared decision making during the MAGIC program, to examine the work required to implement shared decision making and to inform future efforts.

methodsThe MAGIC program approached implementation of shared decision making by initiating a range of interventions including: providing workshops; facilitating development of brief decision support tools (Option Grids); initiating a patient activation campaign ('Ask 3 Questions'); gathering feedback using Decision Quality Measures; providing clinical leads meetings, learning events, and feedback sessions; and obtaining executive board level support. At 9 and 15 months (May and November 2011), two rounds of semi-structured interviews were conducted with healthcare professionals in three secondary care teams to explore views on the impact of these interventions. Interview data were coded by two reviewers using a framework derived from the Normalization Process Theory.

resultsA total of 54 interviews were completed with 31 healthcare professionals. Partial implementation of shared decision making could be explained using the four components of the Normalization Process Theory: 'coherence,' 'cognitive participation,' 'collective action,' and 'reflexive monitoring.' Shared decision making was integrated into routine practice when clinical teams shared coherent views of role and purpose ('coherence'). Shared decision making was facilitated when teams engaged in developing and delivering interventions ('cognitive participation'), and when those interventions fit with existing skill sets and organizational priorities ('collective action') resulting in demonstrable improvements to practice ('reflexive monitoring'). The implementation process uncovered diverse and conflicting attitudes toward shared decision making; 'coherence' was often missing.

conclusionsThe study showed that implementation of shared decision making is more complex than the delivery of patient decision support interventions to patients, a portrayal that often goes unquestioned. Normalizing shared decision making requires intensive work to ensure teams have a shared understanding of the purpose of involving patients in decisions, and undergo the attitudinal shifts that many health professionals feel are required when comprehension goes beyond initial interpretations. Divergent views on the value of engaging patients in decisions remain a significant barrier to implementation.

Indexed as

Decision MakingPatient ParticipationCooperative BehaviorHumansModels, TheoreticalPatient-Centered CareProgram DevelopmentProgram EvaluationQualitative ResearchUnited Kingdom

Identifiers

PMID24006959
PMCPMC3848565

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Registered trials

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.