Workplace inequities and health outcomes among Black professionals in Canada
Bibliographic record
Abstract
BACKGROUND: Anti-Black racism in Canada remains a significant barrier to the career advancement and overall well-being of Black professionals. Despite the existence of policies and legislation aimed at reducing workplace discrimination, Black Canadians continue to face systemic racism, microaggressions, and various forms of discrimination that hinder their professional growth and contribute to a hostile work environment. OBJECTIVE: This study explores the specific manifestations of anti-Black racism in Canadian workplaces, examines the physical and mental health impacts on Black professionals, and investigates the responses and coping mechanisms employed by these individuals in the face of racism. METHODS: A qualitative study was conducted involving semi-structured interviews with 24 Black professionals from diverse sectors, including healthcare, information technology, academia, and public service. Participants were selected based on their professional experience and self-identification as Black. Data were collected through in-depth interviews, which were transcribed and analyzed using LeximancerTM software to identify recurring themes and patterns. RESULTS: The study identified three primary themes: (1) Mechanisms of anti-Black racism, including microaggressions, overt bias, and tokenism; (2) Impacts of anti-Black racism, such as mental health trauma, career stagnation, and exacerbation of chronic health conditions; and (3) Responses of Black professionals, including code-switching, self-preservation behaviors, and early exit from the workplace. The findings reveal that despite high academic achievement and leadership positions, Black professionals face persistent discrimination that affects their career trajectories and personal lives. CONCLUSION: Anti-Black racism in Canadian workplaces is deeply entrenched and continues to negatively impact the lives and careers of Black professionals. The study highlights the need for more effective diversity and inclusion initiatives that address the root causes of racism. Further research is recommended to explore the economic and psychological impacts of anti-Black racism and to develop strategies to mitigate its effects in the workplace.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".