Exploring BAME Student Experiences in Healthcare Courses in the United Kingdom: A Systematic Review.
Bibliographic record
Abstract
Introduction: Black, Asian, and Minority Ethnic (BAME) students in healthcare-related courses are exposed to various challenging experiences compared to their White counterparts, not only in the UK (United Kingdom) but across the globe. Underachieving, stereotyping, racial bias, and cultural differences, among other experiences, hinder their medical education, practice, and attainment. This review aimed to explore and understand the experiences of BAME students enrolled in healthcare related courses in the United Kingdom. Methods: Computerised bibliographic search was carried out using MeSH and free text descriptors via PubMed, Cochrane, Google Scholar, and Science Direct for eligible English-published studies exploring BAME experiences in the UK from 2010-2023. Results: A cumulative total of 813 studies were obtained from the literature search, of which five met the inclusion criteria. Quality assessment for risk of bias was assessed using the Newcastle Ottawa scale, yielding one study of satisfactory quality, while four were deemed to be of good quality. Conclusion: BAME students pursuing health-related courses across the UK. face a range of experiences, including racial discrimination, unconscious bias, and a lack of representation and support. Additionally, BAME students are more likely to report incidents of racial harassment and withdraw from their respective courses as well as experiencing mental health issues due to their experiences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".