Evidence map›Paper›PMID 28159008›Full record

ArticleHealth research policy and systems2017

Unravelling networks in local public health policymaking in three European countries - a systems analysis.

Hilde P E M Spitters, Cathrine J Lau, Petru Sandu, Marcel Quanjel, Diana Dulf, Charlotte Glümer, Hans A M van Oers, Ien A M van de Goor

Abstract readMulticenter Study
In one paragraph

Article in Health research policy and systems, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Analysis of the Local Health-Enhancing Physical Activity Policies on the French Riviera.International journal of environmental research and public health · 2020
    Article
  5. Article
  6. 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

8 authors.

Hilde P E M SpittersDepartment of Tranzo, Tilburg School of Social and Behavioral Sciences, Tilburg University, P.O. Box 90153, 5000 LE, Tilburg, The Netherlands. h.p.e.m.spitters@tilburguniversity.edu.
Cathrine J LauResearch Centre for Prevention and Health, Capital Region of Denmark, Ndr, Ringvej 57, Afsnit 84/85, 2600, Glostrup, Denmark.
Petru SanduDepartment of Public Health, College of Political, Administrative and Communication Sciences, Babes-Bolyai University, 7 Pandurilor St. Universitas, Room 910, 400376, Cluj-Napoca, Romania.
Marcel QuanjelYouth Care, Special Needs Education and Research, P.O. Box 6546, 6503 GA, Nijmegen, The Netherlands.
Diana DulfDepartment of Public Health, College of Political, Administrative and Communication Sciences, Babes-Bolyai University, 7 Pandurilor St. Universitas, Room 910, 400376, Cluj-Napoca, Romania.
Charlotte GlümerResearch Centre for Prevention and Health, Capital Region of Denmark, Ndr, Ringvej 57, Afsnit 84/85, 2600, Glostrup, Denmark.
Hans A M van OersDepartment of Tranzo, Tilburg School of Social and Behavioral Sciences, Tilburg University, P.O. Box 90153, 5000 LE, Tilburg, The Netherlands.
Ien A M van de GoorDepartment of Tranzo, Tilburg School of Social and Behavioral Sciences, Tilburg University, P.O. Box 90153, 5000 LE, Tilburg, The Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundFacilitating and enhancing interaction between stakeholders involved in the policymaking process to stimulate collaboration and use of evidence, is important to foster the development of effective Health Enhancing Physical Activity (HEPA) policies. Performing an analysis of real-world policymaking processes will help reveal the complexity of a network of stakeholders. Therefore, the main objectives were to unravel the stakeholder network in the policy process by conducting three systems analyses, and to increase insight into the similarities and differences in the policy processes of these European country cases.

methodsA systems analysis of the local HEPA policymaking process was performed in three European countries involved in the 'REsearch into POlicy to enhance Physical Activity' (REPOPA) project, resulting in three schematic models showing the main stakeholders and their relationships. The models were used to compare the systems, focusing on implications with respect to collaboration and use of evidence in local HEPA policymaking. Policy documents and relevant webpages were examined and main stakeholders were interviewed.

resultsThe systems analysis in each country identified the main stakeholders involved and their position and relations in the policymaking process. The Netherlands and Denmark were the most similar and both differed most from Romania, especially at the level of accountability of the local public authorities for local HEPA policymaking. The categories of driving forces underlying the relations between stakeholders were formal relations, informal interaction and knowledge exchange.

conclusionsA systems analysis providing detailed descriptions of positions and relations in the stakeholder network in local level HEPA policymaking is rather unique in this area. The analyses are useful when a need arises for increased interaction, collaboration and use of knowledge between stakeholders in the local HEPA network, as they provide an overview of the stakeholders involved and their mutual relations. This information can be an important starting point to enhance the uptake of evidence and build more effective public health policies.

Indexed as

Health PolicyPolicy MakingPublic HealthCooperative BehaviorDenmarkHumansNetherlandsRomaniaSystems AnalysisTranslational Research, BiomedicalLocal policymaking processPublic healthRelationsSchematic modelStakeholder networkSystems analysis

Identifiers

PMID28159008
PMCPMC5291943

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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.