A report based on Freedom of Information data from 18 acute NHS trusts in London has found that White applicants were 3.7 times more likely to be offered nursing, midwifery and health care assistant posts than applicants from Black, Asian, Mixed or Other ethnic backgrounds.
The study, published by consultancy Citou Consulting and titled Corporate Gaslighting Nursing, Midwifery and Health Care Assistants, analysed recruitment rounds in 2025. It is the third in a series of studies following earlier reports in 2021 and 2025, and draws on trust supplied data on applications, shortlisting and offers across five workforce categories.
Findings by staff group
The report says the largest disparity was seen among Agenda for Change band 5 nursing posts, where White applicants were 3.68 times as likely to be offered a position. That represents an improvement on last year, when the ratio was 5.64, but is more than double the 1.68 ratio recorded in 2020 to 2021, according to the consultancy.
For band 6 midwifery posts the ratio of White to minority ethnic applicants offered a role was 3.2, up about 21 per cent on the previous year. At senior clinical grades, band 8c and above, the ratio rose to 2.97 from 1.68 in the earlier period covered by the series.
The analysis covers band 5 nurses, band 8c and above nurses, band 6 midwives, health care assistants at bands 2 to 3, and bank nurses at band 5. Overall, the report finds White applicants were 3.7 times more likely to receive offers than those from Black, Asian, Mixed or Other ethnic groups.
Recommendations and pilot results
One of the report’s principal recommendations is wider use of batch recruitment, in which band 5 nurses are hired as a pooled group for a whole trust rather than on an ad hoc basis for individual vacancies. The consultancy says a London pilot of that approach raised the offer rate from 12.3 per cent to 36.1 per cent of applicants.
The report urges trusts and regional bodies to consider adopting the batch recruitment model more widely as a means of narrowing the gaps identified in the data.
The author of the report, Sheila Cunliffe, noted that NHS England treats a ratio between 0.8 and 1.25 as acceptable for its own recruitment measure. The report interprets the statistical disparities as potential evidence of unequal treatment; those characterisations are the author’s interpretation of the data.
NHS England’s regional chief nurse for London, Karen Bonner, is quoted in the report describing recruitment and progression inequalities as an intractable issue, and saying racial disparities in recruitment and progression remain unacceptable. The Department of Health and Social Care told the consultancy that NHS recruitment must comply with the Equality Act 2010.
The consultancy’s analysis was compiled from information supplied by 18 acute trusts under Freedom of Information requests. The report itself is not peer reviewed, and the methodology differs from the NHS Workforce Race Equality Standard, which measures from shortlisting rather than from the initial application.
The report was shared exclusively with a nursing publication, and cites responses from NHS England London and the Department of Health within its text. That means there is no independently published second news account accompanying the consultancy’s findings.
Campaigners and some union leaders have previously called for greater transparency and reform in recruitment practices after earlier studies. This report gives fresh numerical detail on where gaps between White and minority ethnic applicants appear to be widest in London, and sets out a set of measures it says could be piloted more widely to increase offer rates among minority ethnic candidates.
Given the methodological differences and the source of the data, the consultancy urges trusts, regulators and policy makers to examine the figures and consider changes to recruitment practice that could reduce disparities. The report concludes by recommending further scrutiny and the evaluation of batch recruitment as a practical step to improve offer rates for underrepresented applicants.
