Flames of transformation: Igniting better mental and physical health for racialized and gendered North Americans
Bibliographic record
Abstract
COVID-19 is catalyzing both crises and opportunities for communities of color. The crisis of high mental and physical morbidities and mortalities exposes persistent inequities while providing opportunities to celebrate the power of rejuvenated anti-racism movements, fueled partly in response to the extremism of ultra-conservative governments, the circumstances to reflect deeply on racism because of forced stay-at-home-orders, and digital technologies primarily driven by youth. In marking this historical moment of longstanding anti-racism and decolonial struggles, I assert the importance of foregrounding women's needs. In analyzing racism, rooted in colonialism and white supremacy, and its impacts on mental and physical health status, I focus on improving racialized women's lives within the larger context, concentrating on the determinants of health. I contend that fanning the flames to scathe the racist and sexist foundations of North American society will break new ground for sharing wealth, bolstering solidarity and sisterhood, and ultimately improving Black, Indigenous, and Women of Color (BIWOC) health. Canadian BIWOC earn approximately 59 cents to the dollar earned by non-racialized men, creating vulnerabilities to economic downturns, such as the one Canada is currently in. BIWOC care aides, at the bottom of the healthcare hierarchy, are emblematic of other Black, Indigenous, and People of Color (BIPOC), who face risks of frontline work, low wages, poor job security, unpaid sick days and so forth. To that end, policy recommendations include employment equity initiatives that hire groups of racialized women who consciously express solidarity with each other. Cultural shifts within institutions will be key to providing safe environments. Improving food security, internet access and BIWOC-related data collection linked to community-based programming while prioritizing research on BIWOC will go a long way toward improving BIWOC health. Addressing racism and sexism within the healthcare system, aiming for equitable diagnostic and treatment foci, will require transformative efforts including determined leadership and buy-in from all levels of staff, long-term training and evaluation programs, audited by BIPOC communities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".