Camouflaged prejudice and the affirmation of skin colour differences: assessment of racism among Brazilian undergraduate dental students
Bibliographic record
Abstract
The negative oral health outcomes of disadvantaged racial groups have been well-documented, as racial disparity in oral health persists over time and in different locations1. However, it is important to note that skin colour has no biological meaning, and the observed differences can be physiological expressions of social injustice such as racism. Aim: The aim of this study was to analyse the association between levels of modern racism (camouflaged prejudice and affirmation of differences) and sociodemographic characteristics of Brazilian dental students. Material and Method: An epidemiological cross-sectional online survey was conducted on 441 Brazilian undergraduate dental students using Google Forms. Participants were recruited via emails and social media, using the snowball technique. The Checklist for Reporting of Survey Studies (CROSS) was followed. The survey used sociodemographic variables, and the Brazilian version of the Modern Racism Scale (B-MRS), which measures the cognitive component of subtle racial attitudes. The scale assesses the central notion of disguised prejudice and has two domains: 'denial of prejudice' and 'affirmation of differences'. Participants' self-declared skin colour was categorized as "white" and "non-white" (black, brown, indigenous, yellow). Univariate analysis and Poisson regression with robust variance were applied. Results: Participants' mean age was 24.1 years (±5.4). Most participants were self-declared as white (54%) and 46% as non-white skin colour. Higher B-MRS overall-scores were observed in male (p=0.008) and non-white (p=0.002) students. B-MRS scores for the domain 'affirmation of differences' (representation of those who believe that whites and non-whites are truly different) were higher among male dental students (PR=1.138; CI 95%: 1.019-1.271) and those from low-income families (PR=1.306; CI 95%: 1.089-1.565). Scores for the domain 'denial of prejudice' (the idea that non-whites use their race to receive legal benefits) were higher among male dental students (PR=1.328; CI 95%: 1.129-1.562). Conclusions: In general, male non-white students had higher modern racism indicators. Male students from low-income families believed that whites and non-whites are truly different, accounting for the affirmation of difference in this sample.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".