Workplace racial discrimination as a major public health threat: Association with depression, anxiety, stress and PTSD symptoms among racialized and Indigenous peoples in Canada
Bibliographic record
Abstract
OBJECTIVE: Workplace racial discrimination (WRD) is a well-recognized issue; its impacts remain limitedly explored. This study examines the rate of WRD and its association with mental health among Arab, Asian, Black, and Indigenous individuals in Canada. STUDY DESIGN: Cross-sectional analysis of data from a nationally representative and ethnically diverse survey panel. METHODS: Descriptive and logistic regression analyses were conducted on a subsample of 1535 Arab, Asian, Black, and Indigenous participants aged 25 and older (55.31 % women). RESULTS: Black and Indigenous individuals reported higher rates for both types of workplace racial discrimination. Respectively, Indigenous (OR= 0.62 and OR=1.96, p<0.001) and Black (OR=2.31, p<0.001 and OR=1.60, p=0.006) participants were more likely to experience both racial discrimination in hiring process and in promotion denial or firing compared to Arab participants. Participants who experienced racial discrimination in promotion denial or firing were more likely to present symptoms of anxiety (AOR=2.37, p<0.001, 95 % CI [1.82-3.09]), depression (AOR=2.80, p<0.001, 95 % CI [2.22-3.54]), stress (AOR=2.43, p<0.001, 95 % CI [1.89-3.14]), and PTSD (AOR=2.43, p<0.001, 95 % CI [1.77-3.32]) than those who have not. Similarly, racial discrimination in the hiring process was associated with higher odds of anxiety (AOR=2.24, p<0.001, 95 % CI [1.71-2.92]), depression (AOR=2.18, p<0.001, 95 % CI [1.73-2.75]), stress (AOR=2.01, p<0.001, 95 % CI [1.56-2.59]), and PTSD (AOR=1.99, p<0.001, 95 % CI [1.45-2.72]). CONCLUSIONS: Workplace racial discrimination is strongly associated with mental health problems among Indigenous and racialized individuals. Race-specific organizational interventions and anti-racist programs are recommended to improve workplace well-being and racialized workers health.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".