Evidence map›Paper›PMID 32005252›Full record

ArticleHealth research policy and systems2020

Using a 'rich picture' to facilitate systems thinking in research coproduction.

Kathleen P Conte, Seanna Davidson

Open access · goldAbstract read
In one paragraph

Article in Health research policy and systems, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed
8.8field-weighted citation impact, top 2% 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

24 citing papers in PubMed, 54 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Article
  6. Review
  7. Research Coproduction: An Underused Pathway to Impact.International journal of health policy and management · 2024
    Article
  8. Adolescent social media use: cultivating and constraining competence.International journal of qualitative studies on health and well-being · 2023
    Article
  9. Review
  10. Article
  11. Article
  12. Article
  13. Review
  14. Review
  15. Article
  16. Participatory codesign of patient involvement in a Learning Health System: How can data-driven care be patient-driven care?Health expectations : an international journal of public participation in health care and health policy · 2022
    Article
  17. Article
  18. Article
  19. Building a Prevention System: Infrastructure to Strengthen Health Promotion Outcomes.International journal of environmental research and public health · 2021
    Article
  20. Article
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

2 authors at 2 institutions in 1 country.

Kathleen P ConteThe Australian Prevention Partnership Centre, Sydney, Australia. kathleen.conte@sydney.edu.au.ORCID http://orcid.org/0000-0002-5429-429X
Seanna DavidsonThe Australian Prevention Partnership Centre, Sydney, Australia.
Melbourne School of Theology · AUUniversity of Technology Sydney · AU

Funding

National Health and Medical Research Council (AU) GNT900001
6 · The paper itself

Abstract

backgroundIn coproduction research, traditional 'end-users' are involved in the entire research process. The aim is to facilitate research translation by improving the timeliness and relevance of research. Because end-users often come from multiple sectors and hold diverse perspectives and priorities, involving them in coproduction can be challenging. Tools and approaches are needed to support coproduction teams to successfully navigate divergent viewpoints while producing rigorous but meaningful research outcomes. Rich pictures are a systems thinking tool to help make sense of complexity. In this paper, we describe how we developed and applied a 'rich picture' in a coproduction project with policy-level partners.

methodsGuided by systems thinking principles, we conducted a systemic analysis of ethnographic fieldnotes collected as part of a broader study that examined the dynamics between an IT system and the implementation of the state-wide childhood obesity prevention programmes it was designed to monitor. Translating qualitative themes into metaphor and imagery, we created a visual depiction of the system to reflect the experience of the system's users (health promotion practitioners) and facilitated a workshop with policy-level programme administrators (i.e. participants, n = 7). Our aim was to increase the transparency of the system for our research partners and to spark new insights to improve the quality of programme implementation.

resultsGuided by provocative questions, participants discussed and challenged each other's thinking on the current functioning of the system. They identified future lines of inquiry to explore for quality improvement. Participants strongly agreed that the picture was a constructive way to engage with the ethnographic data but were challenged by the information and its implications. The opportunity for participants to co-learn from each other as well as from the picture was an added value.

conclusionIn the context of the facilitated workshop, the rich picture enabled research partners to engage with complex research findings and gain new insights. Its value was harnessed via the guided participatory process. This demonstrates the importance that, in the future, such tools should be accompanied by practices that enable participants to think with and apply systems thinking concepts and principles.

Indexed as

Systems AnalysisAnthropology, CulturalHealth EducatorsHealth Information SystemsHealth PromotionHumansPediatric ObesityResearch PersonnelTranslational Research, Biomedicalcoproductionhealth information technologyhealth promotionquality improvementsoft systems methodologySystems thinkingtranslation

Identifiers

PMID32005252
PMCPMC6995183
OpenAlexW3010764642

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.