Sarindi Aryasinghe, Catalina Carenzo, Kerri-Ann Barnett, Rabia Khalid, Koya Greenaway-Harvey, Colleen Sherlock, Louise Clark, Kevin Croft, Tim Orchard, Erik Mayer
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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.</p><p><strong>Methods: </strong>Using 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.</p><p><strong>Results: </strong>The 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.</p><p><strong>Conclusions: </strong>The 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.</p>","PeriodicalId":39823,"journal":{"name":"Human Resources for Health","volume":"23 1","pages":"24"},"PeriodicalIF":3.9000,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12096476/pdf/","citationCount":"0","resultStr":"{\"title\":\"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.\",\"authors\":\"Sarindi Aryasinghe, Catalina Carenzo, Kerri-Ann Barnett, Rabia Khalid, Koya Greenaway-Harvey, Colleen Sherlock, Louise Clark, Kevin Croft, Tim Orchard, Erik Mayer\",\"doi\":\"10.1186/s12960-025-00991-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>The 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.</p><p><strong>Methods: </strong>Using 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.</p><p><strong>Results: </strong>The 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. 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引用次数: 0
摘要
背景:英国国家医疗服务体系(NHS)努力争取一个公平、多样化和包容性的工作场所,但黑人和少数民族(BME)在高级领导角色中的代表性仍然有限。为了解决这个问题,一个大型的多医院急性NHS信托引入了一项包容性招聘计划,要求种族和性别多样化的面试小组和给首席执行官(CEO)的一封信,解释招聘经理的候选人选择。这产生了大量有价值的结构化和自由文本数据,但是手动分析以获得可操作的见解是具有挑战性的,限制了评估和改进这种平等、多样性和包容性(EDI)招聘计划的努力。方法:利用从2021年9月至2024年1月定期收集的该项目招聘数据,我们使用自然语言处理人工智能技术,结合二次数据分析进行三角测量,评估该项目在增加BME任命到高级领导职位的数量方面的有效性。多元逻辑回归确定了与白人候选人相比,影响BME候选人申请、入围或获得职位的几率的招聘因素。话题和情感分析揭示了招聘经理和候选人特征分层的候选人评估的主题趋势和基调。标准化的平均面试分数也与工作等级和候选人特征进行了比较。结果:要求招聘经理写信给首席执行官解释招聘决定,与白人候选人相比,BME候选人获得职位的几率提高了1.7倍[95% CI 1.2-2.3]。然而,白人候选人获得高级职位的总体几率仍然更高。BME候选人在面试中得分较低,其中BME女性的可能性是其两倍(p结论:包容性招聘计划的致首席执行官信部分增加了BME在高级领导角色中的代表性,但招聘过程中仍然存在不平等现象,反映了国家NHS招聘趋势。虽然该倡议标志着进展,但需要进一步的战略来确保公平征聘、职业发展和留用。人工智能工具,例如自然语言处理,通过分析常规收集的招聘数据,提供有效的方法来评估和加强EDI招聘计划,以确定需要改进的地方,并建立最佳做法。
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.
Background: The 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.
Methods: Using 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.
Results: The 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.
Conclusions: The 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.
期刊介绍:
Human Resources for Health is an open access, peer-reviewed, online journal covering all aspects of planning, producing and managing the health workforce - all those who provide health services worldwide. Human Resources for Health aims to disseminate research on health workforce policy, the health labour market, health workforce practice, development of knowledge tools and implementation mechanisms nationally and internationally; as well as specific features of the health workforce, such as the impact of management of health workers" performance and its link with health outcomes. The journal encourages debate on health sector reforms and their link with human resources issues, a hitherto-neglected area.