Evidence map›Paper›PMID 36923666›Full record

ArticleJournal of research in nursing : JRN2023

Developing a clinical academic career pathway in a Community and Mental Health NHS Trust.

Lizelle Bernhardt, Sarah Baillon, Susan M Corr, Kay de Vries

Abstract read
In one paragraph

Article in Journal of research in nursing : JRN, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Review
  3. 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

4 authors.

Lizelle BernhardtClinical Academic Careers Pathway Lead, Leicestershire Partnership NHS Trust, Leicester, UK.ORCID https://orcid.org/0000-0002-6010-3955
Sarah BaillonSenior Research Associate, Leicestershire Partnership NHS Trust, Leicester, UK.
Susan M CorrIndependent Coach/Consultant specialising in Clinical Academic Careers; Former Head of Research and Development, Leicestershire Partnership NHS Trust (formerly), Leicester, UK.
Kay de VriesProfessor of Older Peoples Health, De Montfort University, Leicester, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Despite growing evidence of the impact that clinical academic (CA) staff have on patient care and clinical practice, there are disproportionately low numbers of nurses, allied health professionals (AHPs) and other healthcare professionals in CA joint roles, compared to their medical colleagues. Aim: To describe the initial development of a CA career pathway for nurses, AHPs and other healthcare professionals in a Community and Mental Health NHS Trust. Methods: Kotter's 8-Step Change Model was used to expand opportunities and research culture across an NHS Trust. Results: A variety of capacity and capability initiatives at different academic levels were created to support CA development and to complement those available externally. These opportunities were underpinned by a research and development strategy, senior leadership buy-in, manager and clinical staff support, and targeted organisation-wide communication. Conclusion: The ongoing development of innovative CA opportunities in the Trust, alongside greater support for staff interested in pursuing CA careers, has resulted in a growing number of individuals successful in developing as CAs. This has led to a growth in research culture in the organisation and a greater understanding of what CA staff can bring to patient care, the clinical service and the wider organisation.

Indexed as

allied health professionalscareer pathwayclinical academicNHSnursing

Identifiers

PMID36923666
PMCPMC10009083

What OpenQuestion holds

Textmetadata
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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.