COVID-19 and the Intersection of Race, Ethnicity, and Sexual Minority Status.
Bibliographic record
Abstract
Abstract The COVID-19 virus has caused millions of deaths and impaired physical and mental health and social disconnection for countless persons around the world; concomitantly, the pandemic has exposed and exacerbated the pervasive effects of racism and stigma experienced by Black, Indigenous, or People of Color (BIPOC) and other marginalized/stigmatized groups. This study adopts an intersectional perspective examining multiple marginalized identities (i.e., the combination of LGBTQ and BIPOC status) and COVID-19 pandemic health stressors. We report on data from an online survey (conducted between Aug 10 and Oct. 10, 2020) focusing on current experiences and future planning during the COVID-19 pandemic in Canada. LGBTQ respondents (n=415) indicated significantly higher levels of depression, loneliness, sadness, and isolation in comparison to heterosexuals (n=3916). Heterosexual white respondents (n=3446) reported significantly higher levels of acceptance in their community and reported greater happiness but also higher rates of feeling of isolation than heterosexual BIPOC heterosexuals (n=470) who reported significantly higher rates of feeling judged/shamed by others than the heterosexual white respondents. In contrast to our expectations, white LGBTQ respondents (n=366) reported significantly more depression, loneliness, anxiety, and sadness than their BIPOC LGBTQ peers (n=49). These findings are interpreted as reflecting a complex mix of the effects of marginalization (as experienced by LGBTQ persons in general), and privilege and relative deprivation (as experienced by heterosexual and LGBTQ white persons) along with resilience and the moderated expectations and experiences of BIPOC LGBTQ persons.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".