Evidence map›Paper›PMID 35550164›Full record

Trial reportImplementation science : IS2022

Exploring a novel method for optimising the implementation of a colorectal cancer risk prediction tool into primary care: a qualitative study.

Shakira Milton, Jon D Emery, Jane Rinaldi, Joanne Kinder, Adrian Bickerstaffe, Sibel Saya, Mark A Jenkins, Jennifer McIntosh

Open access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Implementation science : IS, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.7field-weighted citation impact, top 14% of its field
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

7 citing papers in PubMed, 17 citations in OpenAlex.

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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

8 authors at 1 institution in 3 countries.

Shakira MiltonCentre for Cancer Research, University of Melbourne, Melbourne, Australia. shakira.milton@unimelb.edu.au.ORCID 0000-0002-8510-6351
Jon D EmeryCentre for Cancer Research, University of Melbourne, Melbourne, Australia.
Jane RinaldiUniversity of Melbourne Shepparton Medical Centre, Melbourne Teaching Health Clinics Ltd, 49 Graham Street, Shepparton, VIC, 3630, Australia.
Joanne KinderUniversity of Melbourne Shepparton Medical Centre, Melbourne Teaching Health Clinics Ltd, 49 Graham Street, Shepparton, VIC, 3630, Australia.
Adrian BickerstaffeCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia.
Sibel SayaCentre for Cancer Research, University of Melbourne, Melbourne, Australia.
Mark A JenkinsCentre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Parkville, Victoria, Australia.
Jennifer McIntoshDepartment of General Practice, University of Melbourne, Melbourne, Australia.
The University of Melbourne · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWe developed a colorectal cancer risk prediction tool ('CRISP') to provide individualised risk-based advice for colorectal cancer screening. Using known environmental, behavioural, and familial risk factors, CRISP was designed to facilitate tailored screening advice to patients aged 50 to 74 years in general practice. In parallel to a randomised controlled trial of the CRISP tool, we developed and evaluated an evidence-based implementation strategy.

methodsQualitative methods were used to explore the implementation of CRISP in general practice. Using one general practice in regional Victoria, Australia, as a 'laboratory', we tested ways to embed CRISP into routine clinical practice. General practitioners, nurses, and operations manager co-designed the implementation methods with researchers, focussing on existing practice processes that would be sustainable. Researchers interviewed the staff regularly to assess the successfulness of the strategies employed, and implementation methods were adapted throughout the study period in response to feedback from qualitative interviews. The Consolidated Framework for Implementation Research (CFIR) underpinned the development of the interview guide and intervention strategy. Coding was inductive and themes were developed through consensus between the authors. Emerging themes were mapped onto the CFIR domains and a fidelity checklist was developed to ensure CRISP was being used as intended.

resultsBetween December 2016 and September 2019, 1 interviews were conducted, both face-to-face and via videoconferencing (Zoom). All interviews were transcribed verbatim and coded. Themes were mapped onto the following CFIR domains: (1) 'characteristics of the intervention': CRISP was valued but time consuming; (2) 'inner setting': the practice was open to changing systems; 3. 'outer setting': CRISP helped facilitate screening; (4) 'individual characteristics': the practice staff were adaptable and able to facilitate adoption of new clinical processes; and (5) 'processes': fidelity checking, and education was important.

conclusionsThese results describe a novel method for exploring implementation strategies for a colorectal cancer risk prediction tool in the context of a parallel RCT testing clinical efficacy. The study identified successful and unsuccessful implementation strategies using an adaptive methodology over time. This method emphasised the importance of co-design input to make an intervention like CRISP sustainable for use in other practices and with other risk tools.

Indexed as

Colorectal NeoplasmsGeneral PractitionersHumansPrimary Health CareQualitative ResearchVictoriaColorectal cancer screeningGeneral practiceImplementation sciencePrimary careRisk prediction tool

Identifiers

PMID35550164
PMCPMC9097304
OpenAlexW4280628867

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.