Evidence map›Paper›PMID 38641648›Full record

ArticleHealth research policy and systems2024

National strategies for knowledge translation in health policy-making: A scoping review of grey literature.

Balázs Babarczy, Julia Scarlett, Tarang Sharma, Péter Gaál, Balázs Szécsényi-Nagy, Tanja Kuchenmüller

Abstract readScoping Review
In one paragraph

Article in Health research policy and systems, 2024. 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
–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

7 citing papers in PubMed.

  1. Article
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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

6 authors.

Balázs BabarczyUnit of Knowledge Management, Evidence and Research for Policy-Making, Division of Information, Evidence, Research and Innovation, World Health Organization Regional Office for Europe, UN City, Marmorvej 51, 2100, Copenhagen Ø, Denmark.
Julia ScarlettUnit of Knowledge Management, Evidence and Research for Policy-Making, Division of Information, Evidence, Research and Innovation, World Health Organization Regional Office for Europe, UN City, Marmorvej 51, 2100, Copenhagen Ø, Denmark.
Tarang SharmaUnit of Knowledge Management, Evidence and Research for Policy-Making, Division of Information, Evidence, Research and Innovation, World Health Organization Regional Office for Europe, UN City, Marmorvej 51, 2100, Copenhagen Ø, Denmark.
Péter GaálHealth Services Management Training Centre, Semmelweis University Health Services Management Training Centre, Kútvölgyi Út 2, Budapest, 1125, Hungary. gaal.peter@emk.semmelweis.hu.ORCID http://orcid.org/0000-0002-6815-9021
Balázs Szécsényi-NagyHealth Services Management Training Centre, Semmelweis University Health Services Management Training Centre, Kútvölgyi Út 2, Budapest, 1125, Hungary.
Tanja KuchenmüllerUnit of Knowledge Management, Evidence and Research for Policy-Making, Division of Information, Evidence, Research and Innovation, World Health Organization Regional Office for Europe, UN City, Marmorvej 51, 2100, Copenhagen Ø, Denmark.

Funding

World Health Organization 001
6 · The paper itself

Abstract

BACKGROUND AND

objectivesWithout strategic actions in its support, the translation of scientific research evidence into health policy is often absent or delayed. This review systematically maps and assesses national-level strategic documents in the field of knowledge translation (KT) for health policy, and develops a practical template that can support Evidence-informed Policy Network (EVIPNet) Europe countries in producing national strategies for evidence-informed policy-making.

methodsWebsites of organizations with strategic responsibilities in KT were electronically searched, on the basis of pre-defined criteria, in July-August 2017, and an updated search was carried out in April-June 2021. We included national strategies or elements of national strategies that dealt with KT activities, as well as similar strategies of individual institutions with a national policy focus. Two reviewers screened the strategies for inclusion. Data were analysed using qualitative content analysis.

resultsA total of 65 unique documents were identified, of which 17 were eligible and analysed for their structure and content. Of the 17, 1 document was a national health KT action plan and 6 documents were institution-level KT strategies. The remaining 10 strategies, which were also included were 2 national health strategies, 5 national health research strategies and 3 national KT strategies (not specific to the field of health alone). In all, 13 structural elements and 7 major themes of health policy KT strategies were identified from the included documents.

conclusionKT in health policy, as emerged from the national strategies that our mapping identified, is based on the production and accessibility of policy-relevant research, its packaging for policy-making and the activities related to knowledge exchange. KT strategies may play different roles in the complex and context-specific process of policy-making. Our findings show that the main ideas of health-specific evidence-informed policy literature appear in these strategies, but their effectiveness depends on the way stakeholders use them. Specific knowledge-brokering institutions and organizational capacity, advocacy about the use of evidence, and close collaboration and co-decision-making with key stakeholders are essential in furthering the policy uptake of research results.

Indexed as

Gray LiteratureTranslational Science, BiomedicalHealth PolicyHumansPolicy MakingTranslational Research, BiomedicalEvidence-informed policy-makingEVIPNet EuropeHealth planningKnowledge translation

Identifiers

PMID38641648
PMCPMC11031914

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.