Evidence map›Paper›PMID 40405205›Full record

ArticleHuman resources for health2025

Increasing the ethnic diversity of senior leadership within the English National Health Service: using an artificial intelligence approach to evaluate inclusive recruitment strategies in hospital settings.

Sarindi Aryasinghe, Catalina Carenzo, Kerri-Ann Barnett, Rabia Khalid, Koya Greenaway-Harvey, Colleen Sherlock, Louise Clark, Kevin Croft, Tim Orchard, Erik Mayer

Abstract read
In one paragraph

Article in Human resources for health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

10 authors.

Sarindi AryasingheiCARE Secure Data Environment, NIHR Imperial Biomedical Research Centre, Imperial College Healthcare NHS Trust, London, UK. s.aryasinghe@imperial.ac.uk.
Catalina CarenzoiCARE Secure Data Environment, NIHR Imperial Biomedical Research Centre, Imperial College Healthcare NHS Trust, London, UK.
Kerri-Ann BarnettOffice of the Chief Executive and People and Organisational Development Directorate, Imperial College Healthcare NHS Trust, London, UK.
Rabia KhalidOffice of the Chief Executive and People and Organisational Development Directorate, Imperial College Healthcare NHS Trust, London, UK.
Koya Greenaway-HarveyOffice of the Chief Executive and People and Organisational Development Directorate, Imperial College Healthcare NHS Trust, London, UK.
Colleen SherlockOffice of the Chief Executive and People and Organisational Development Directorate, Imperial College Healthcare NHS Trust, London, UK.
Louise ClarkOffice of the Chief Executive and People and Organisational Development Directorate, Imperial College Healthcare NHS Trust, London, UK. louise.clark20@nhs.net.
Kevin CroftOffice of the Chief Executive and People and Organisational Development Directorate, Imperial College Healthcare NHS Trust, London, UK.
Tim OrchardOffice of the Chief Executive and People and Organisational Development Directorate, Imperial College Healthcare NHS Trust, London, UK.
Erik MayeriCARE Secure Data Environment, NIHR Imperial Biomedical Research Centre, Imperial College Healthcare NHS Trust, London, UK.

Funding

National Institute for Health Research (NIHR) Imperial Biomedical Research Centre NIHR203323
6 · The paper itself

Abstract

backgroundThe English National Health Service (NHS) strives for a fair, diverse, and inclusive workplace, but Black and Minority Ethnic (BME) representation in senior leadership roles remains limited. To address this, a large multi-hospital acute NHS Trust introduced an inclusive recruitment programme, requiring ethnically and gender diverse interview panels and a letter to the Chief Executive Officer (CEO) explaining hiring manager's candidate choice. This generated large amount of valuable structured and free-text data, but manual analysis to derive actionable insights is challenging, limiting efforts to evaluate and improve such equality, diversity, and inclusion (EDI) recruitment initiatives.

methodsUsing this routinely collected recruitment data from the programme between September 2021 to January 2024, we used natural language processing artificial intelligence techniques, triangulated with secondary data analysis, to evaluate the programme's effectiveness in increasing the number of BME appointees to senior leadership roles. Multivariate logistic regression identified recruitment factors that influence the odds of BME candidates applying, being shortlisted or offered a role compared to white candidates. Topic and sentiment analysis revealed thematic trends and tone of candidate assessments, stratified by hiring manager and candidate characteristics. Normalised average interview scores were also compared by job grades and candidate characteristics.

resultsThe requirement for hiring managers to write a letter to the CEO explaining recruitment decisions raised the odds of a BME candidate being offered a role by 1.7 times [95% CI 1.2-2.3] compared to white candidates. However, white candidates still had higher overall odds of being offered senior roles. BME candidates scored lower in interviews, with BME women twice as likely (p < 0.05) to receive negative assessments compared to white women.

conclusionsThe Letter to the CEO component of the inclusive recruitment programme increased BME representation in senior leadership roles, but inequities still persist in the recruitment process, reflecting national NHS recruitment trends. While the initiative marks progress, further strategies are needed to ensure equitable recruitment, career development, and retention. Artificial intelligence tools, such as natural language processing, provide effective methods to evaluate and enhance EDI recruitment initiatives by analysing routinely collected recruitment data to identify areas for improvement and establish best practices.

Indexed as

Artificial IntelligenceCultural DiversityEthnicityLeadershipMinority GroupsPersonnel SelectionState MedicineEnglandFemaleHumansMaleUnited KingdomArtificial intelligenceDiscriminationDiversityEqualityEthnicityHealth leadershipHuman resource analyticsInclusionNatural language processingRecruitment

Identifiers

PMID40405205
PMCPMC12096476

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.