Evidence map›Paper›PMID 29207955›Full record

ArticleBMC medical research methodology2017

Classification schemes for knowledge translation interventions: a practical resource for researchers.

Susan E Slaughter, Gabrielle L Zimmermann, Megan Nuspl, Heather M Hanson, Lauren Albrecht, Rosmin Esmail, Khara Sauro, Amanda S Newton, Maoliosa Donald, Michele P Dyson and 2 more

Open access · goldAbstract read
In one paragraph

Article in BMC medical research methodology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 2 pooled it
3.0field-weighted citation impact, top 8% 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

15 citing papers in PubMed, 2 syntheses or guidelines pooled it, 27 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

12 authors at 3 institutions in 1 country.

Susan E SlaughterUniversity of Alberta, Edmonton, Canada. sslaught@ualberta.ca.
Gabrielle L ZimmermannAlberta SPOR SUPPORT Unit KT Platform, Edmonton, Canada.
Megan NusplAlberta SPOR SUPPORT Unit KT Platform, Edmonton, Canada.
Heather M HansonUniversity of Calgary, Calgary, Canada.
Lauren AlbrechtUniversity of Alberta, Edmonton, Canada.
Rosmin EsmailUniversity of Calgary, Calgary, Canada.
Khara SauroAlberta Health Services, Calgary, Canada.
Amanda S NewtonUniversity of Alberta, Edmonton, Canada.
Maoliosa DonaldUniversity of Calgary, Calgary, Canada.
Michele P DysonUniversity of Alberta, Edmonton, Canada.
Denise ThomsonAlberta SPOR SUPPORT Unit KT Platform, Edmonton, Canada.
Lisa HartlingUniversity of Alberta, Edmonton, Canada.
University of Alberta · CAAlberta Health Services · CAUniversity of Calgary · CA

Funding

Alberta SPOR SUPPORT Unit KT Platform not applicable
6 · The paper itself

Abstract

backgroundAs implementation science advances, the number of interventions to promote the translation of evidence into healthcare, health systems, or health policy is growing. Accordingly, classification schemes for these knowledge translation (KT) interventions have emerged. A recent scoping review identified 51 classification schemes of KT interventions to integrate evidence into healthcare practice; however, the review did not evaluate the quality of the classification schemes or provide detailed information to assist researchers in selecting a scheme for their context and purpose. This study aimed to further examine and assess the quality of these classification schemes of KT interventions, and provide information to aid researchers when selecting a classification scheme.

methodsWe abstracted the following information from each of the original 51 classification scheme articles: authors' objectives; purpose of the scheme and field of application; socioecologic level (individual, organizational, community, system); adaptability (broad versus specific); target group (patients, providers, policy-makers), intent (policy, education, practice), and purpose (dissemination versus implementation). Two reviewers independently evaluated the methodological quality of the development of each classification scheme using an adapted version of the AGREE II tool. Based on these assessments, two independent reviewers reached consensus about whether to recommend each scheme for researcher use, or not.

resultsOf the 51 original classification schemes, we excluded seven that were not specific classification schemes, not accessible or duplicates. Of the remaining 44 classification schemes, nine were not recommended. Of the 35 recommended classification schemes, ten focused on behaviour change and six focused on population health. Many schemes (n = 29) addressed practice considerations. Fewer schemes addressed educational or policy objectives. Twenty-five classification schemes had broad applicability, six were specific, and four had elements of both. Twenty-three schemes targeted health providers, nine targeted both patients and providers and one targeted policy-makers. Most classification schemes were intended for implementation rather than dissemination.

conclusionsThirty-five classification schemes of KT interventions were developed and reported with sufficient rigour to be recommended for use by researchers interested in KT in healthcare. Our additional categorization and quality analysis will aid in selecting suitable classification schemes for research initiatives in the field of implementation science.

Indexed as

Delivery of Health CareHumansResearch PersonnelTranslational Research, BiomedicalAGREE II toolClassification schemesHealthcareImplementation scienceKnowledge translation interventions

Identifiers

PMID29207955
PMCPMC5718087
OpenAlexW2774161706

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.