Racial Microaggressions in Healthcare Settings: A Scoping Review
Bibliographic record
Abstract
Aims Racial microaggressions occur when subtle or often automatic exchanges of aversive and covert racism are directed towards people identifying as racialized groups. Consequently, affecting individuals' mental and physical health. Healthcare professionals are a vulnerable group to the effects of racial microaggressions, given the high prevalence of burnout. The aim of the review was to explore healthcare professionals and students' experience of racial microaggressions in healthcare settings Methods A PROSPERO registered scoping review was conducted using the PRISMA extension for scoping review guidelines. The literature search was undertaken in August 2020, of five databases, MEDLINE, EMBASE, CINAHL, PsycINFO, EMCARE and we also searched the ‘grey literature.’ Studies featuring primary data on racialized or migrant microaggressions towards professionals or students in healthcare settings were included. We excluded studies that were not in English. QDA Miner was used to analyse the data, using a non-essentialist perspective, which suggests that ‘culture’ is a movable concept used by different people at different times to suit purposes of identity, politics and science. Results Our search identified 8 papers (5 qualitative, 2 mixed and 1 quantitative) on the experience of microaggressions towards healthcare professionals and students (n = 602). Almost all (87.5%) were conducted in North America and only one (12.5%) in the UK. The primary themes were as follows: Intersectionality: Individual and group social categorizations of race, class, and gender were described as interconnected, leading to interdependent systems of discrimination or disadvantage. Healthcare professionals indicated that increasing diversity and racial representation can reduce bias and thus microaggressions among stakeholders in the culture of work. Workplace culture and lack of senior support: The healthcare curriculum, and the manner of its delivery were found to propagate ideas encouraging racial microaggressions. Seniors behaving as role-models by challenging microaggressions could encourage an open and accountable environment. Supervision was a tool for allyship that reduced the threat of negative race-related incidents. Intervention: Acknowledging racial microaggressions within healthcare, as well as quantifying their presence with tools, encouraged a stronger and more effective response from institutions. Teaching curriculum also served as a useful platform to teach and address microaggressions. Conclusion Racial microaggressions were experienced as having a detrimental impact on healthcare professionals’ well-being and mental health. Consequently, this affected the efficiency, the workplace culture, patient outcomes and job satisfaction. Given the multifaceted nature of racial microaggressions, tackling them requires a complex and wide-ranging response from institutions.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.006 |
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; both teacher heads agree on what is shown here.
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".