Evidence map›Paper›PMID 36405806›Full record

ArticleJournal of research in nursing : JRN2022

Navigating data governance approvals to use routine health and social care data to evidence the hidden population with severe obesity: a case study from a clinical academic's perspective.

Kath Williamson, Amy Nimegeer, Mike Lean

Abstract read
In one paragraph

Article in Journal of research in nursing : JRN, 2022. 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.

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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

3 authors.

Kath WilliamsonNRS Clinician (NHS Lothian), Doctoral Student, Department of Human Nutrition, School of Medicine, Dentistry and Nursing, University of Glasgow, Glasgow, UK.ORCID https://orcid.org/0000-0001-8606-8935
Amy NimegeerResearch Associate (MRC/CSO Social and Public Health Sciences Unit), University of Glasgow, Glasgow, UK.
Mike LeanProfessor of Human Nutrition, Department of Human Nutrition, School of Medicine, Dentistry and Nursing, University of Glasgow, Glasgow, UK.

Funding

Chief Scientist Office SPHSU16Medical Research Council MC_UU_00022/1Medical Research Council MC_UU_12017/15
6 · The paper itself

Abstract

Background: Front-line professionals are uniquely placed to identify evidence gaps and the way routinely-collected data can help address them. This knowledge can enable incisive, clinically-relevant research. Aim: To document an example of the real-world approvals journey within the current NHS/Higher Education regulatory landscape, from the perspective of an experienced nurse undertaking doctoral study as a clinical academic. Methods: An instrumental case-study approach is used to explore the approvals process for a mixed-methods study. Relevant context is highlighted to aid understanding, including introduction of the General Data Protection Regulation and the integration of health and social care services. Results: Formal approvals by nine separate stakeholders from four different organisations took nearly 3 years, including 15 initial or revised applications, assessments or agreements. Obstacles included: conflicting views on what constitutes 'research' or 'service evaluation'; isolated decision-making; fragmented data systems; multiple data controllers and a changing data governance environment. The dual perspectives of being both clinician and academic using routine data are explored. Conclusions: Practitioners face a complex approvals process to use data they routinely collect, for research or evaluation purposes. Use of data during the COVID-19 pandemic has demonstrated the need for streamlining of data governance processes. Practical recommendations are outlined.

Indexed as

approvalsclinical academicdata governanceroutinely-collected datasocial care

Identifiers

PMID36405806
PMCPMC9669932

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.