Fostering Academic Inclusion and Representation: Enhancing Research Capacity for Black Nursing Academics in <scp>UK</scp> Universities—A Qualitative Multi‐Study Protocol
Bibliographic record
Abstract
BACKGROUND: Nursing as a profession remains underrepresented in research leadership, funding success and scholarly authorship globally, which limits its influence on policy and practice. Within this broader context, racially minoritised nursing academics, including Black academics, face additional inequities that further hinder their visibility and progression. Evidence from the United States, Canada and Australia highlights persistent barriers to research careers and leadership opportunities for Black nurses. In the United Kingdom, these disparities are particularly evident: Black nursing academics face barriers to conducting research while in the wider National Health Service workforce, Black nurses are twice less likely than their White counterparts to be promoted. Together, these patterns constrain career progression and hinder the development of culturally competent healthcare education and practice. AIM: To explore the barriers to conducting research among Black nursing academics working in UK universities that are not traditionally research intensive, and to co-create pragmatic, theory-informed recommendations for enabling supportive and equitable research environments. DESIGN: A qualitative multi-study design underpinned by Intersectionality Theory and The Silences Framework. METHODS: Two work packages are proposed. Work Package 1 will use semi-structured interviews to explore the experiences and barriers of conducting research among up to 15 Black nursing academics based at UK universities that are not research-intensive. Work Package 2 will adopt a modified Delphi methodology, engaging key collaborators in two rounds of online codesign workshops. Findings from Work Package 1 will inform structured discussions in which collaborators will develop theory-informed, pragmatic recommendations to strengthen research capacity and engagement among Black nursing academics. CONCLUSION: This study will address the persistent underrepresentation of Black nursing academics in research. While grounded in the UK, the anticipated outputs will have wider applicability, informing policy, shaping institutional strategies and guiding future research priorities across diverse academic and healthcare systems worldwide.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.068 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".