Evidence map›Paper›PMID 23578304›Full record

ArticleImplementation science : IS2013

Improving the normalization of complex interventions: measure development based on normalization process theory (NoMAD): study protocol.

Tracy L Finch, Tim Rapley, Melissa Girling, Frances S Mair, Elizabeth Murray, Shaun Treweek, Elaine McColl, Ian Nicholas Steen, Carl R May

Abstract 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. Cited by 117 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
117citing papers in PubMed, 3 pooled it
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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

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

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57 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

9 authors.

Tracy L FinchInstitute of Health and Society, Newcastle University, Baddiley-Clark Building, Richardson Road, Newcastle-upon-Tyne NE2 4AX, UK. Tracy.Finch@ncl.ac.uk
Tim Rapley
Melissa Girling
Frances S Mair
Elizabeth Murray
Shaun Treweek
Elaine McColl
Ian Nicholas Steen
Carl R May

Funding

Chief Scientist Office HSRU1
6 · The paper itself

Abstract

backgroundUnderstanding implementation processes is key to ensuring that complex interventions in healthcare are taken up in practice and thus maximize intended benefits for service provision and (ultimately) care to patients. Normalization Process Theory (NPT) provides a framework for understanding how a new intervention becomes part of normal practice. This study aims to develop and validate simple generic tools derived from NPT, to be used to improve the implementation of complex healthcare interventions.

objectivesThe objectives of this study are to: develop a set of NPT-based measures and formatively evaluate their use for identifying implementation problems and monitoring progress; conduct preliminary evaluation of these measures across a range of interventions and contexts, and identify factors that affect this process; explore the utility of these measures for predicting outcomes; and develop an online users' manual for the measures.

methodsA combination of qualitative (workshops, item development, user feedback, cognitive interviews) and quantitative (survey) methods will be used to develop NPT measures, and test the utility of the measures in six healthcare intervention settings. DISCUSSION: The measures developed in the study will be available for use by those involved in planning, implementing, and evaluating complex interventions in healthcare and have the potential to enhance the chances of their implementation, leading to sustained changes in working practices.

Indexed as

Diffusion of InnovationData CollectionDelivery of Health CareHealth Services ResearchHumansProgram DevelopmentProgram EvaluationPsychometricsQualitative Research

Identifiers

PMID23578304
PMCPMC3637119

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LicenceCC BY
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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.