What is the effectiveness of antiracist interventions for ethnic minority healthcare staff? A systematic review
Bibliographic record
Abstract
Abstract Background The National Health Service (NHS) has the most diverse workforce in the United Kingdom (UK), 25% (n= 309,532/1,200,000) of staff belong to ethnic minority groups. However, there is evidence of longstanding issues of racism within the NHS and discrimination towards ethnic minority healthcare staff has been rising since 2016. In the first wave of the COVID-19 pandemic, 95% of COVID-19 deaths among doctors were in an ethnic minority group. There has been no definitive answer for the disproportionate COVID-19 mortality but socioeconomic factors due to structural racism have been suggested as the main drivers. No studies have assessed the effectiveness of antiracist interventions for healthcare staff. Methods We conducted a systematic review; databases searched included: AMED, Medline via OVID, CINAHL, APA Pyscinfo, Web of Science and OVID Emcare 25 th – 31 st January 2022. The interventions were structured using a model of antiracist interventions and analysed using narrative synthesis methods. Results 16 papers were reviewed with interventions at different levels: personally mediated (n=9), multilevel (n=4) and institutional (n=3). Personally mediated interventions were workshops (n=8) and a mentorship scheme (n=1). Institutional interventions were policies (n=2) and increasing diversity initiative (n=1). Multilevel interventions were a mix of both. Study designs and risk of bias tools indicated that the quality of evidence was of low quality. Only two studies included control groups. Countries included the USA (n=11), Canada (n=1) and the UK (n=4). Conclusion There is a lack of robust evidence for antiracist interventions for healthcare staff, especially at an institutional level. High quality research is required to evaluate the long-term effects of interventions. Funding statement The Wales COVID-19 Evidence Centre was funded for this work by Health and Care Research Wales on behalf of Welsh Government.
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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 | 0.019 | 0.099 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".