Unpacking racism during COVID-19: narratives from racialized Canadian gay, bisexual, and queer men
Bibliographic record
Abstract
OBJECTIVE: Epidemics impact individuals unevenly across race, gender, and sexuality. In addition to being more vulnerable to COVID-19 infection, evidence suggests racialized gender and sexual minorities experienced disproportionate levels of discrimination and stigma during the COVID-19 epidemic. Drawing on Critical Race Theory (CRT), we examined the experiences of gay, bisexual, queer, and other men who have sex with men (GBQM) of colour facing discrimination during COVID-19. DESIGN: Engage-COVID-19 is a mixed methods study examining the impact of COVID-19 on GBQM living in Vancouver, Toronto, and Montréal, Canada. We conducted two rounds of qualitative interviews (November 2020 to February 2021, and June to October 2021) with 93 GBQM to explore the evolving impact of COVID-19 on their lives. Transcripts were coded using inductive thematic analysis. Data analysis was conducted using Nvivo software. RESULTS: Fifty-nine participants identified as Black, Indigenous, and/or a Person of Colour (BIPOC). These GBQM of colour described multiple experiences of discrimination during COVID-19. Although participants did not report experiences of discrimination based on their sexual identity during COVID-19, we found that experiences of racism affected how they were treated within their sexual networks. Experiences of racism were most often reported by East Asian and Black GBQM. These participants faced racism in public and online spaces, primarily in the form of verbal harassment. Several participants were also harassed because they wore face masks. Verbal abuse against GBQM of colour was largely prompted by racist discourses related to COVID-19. CONCLUSION: Racism remains a pernicious threat to the well-being of GBQM of colour. CRT highlights the importance of assessing how sexualized and gendered discourses about race shape the experiences of GBQM of colour navigating multiple epidemics like COVID-19 and HIV. These pervasive discourses unevenly affect racial and sexual minorities across multiple epidemics, and negatively impact health outcomes for these populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.035 | 0.016 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| 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".