Black Lives Matter in health promotion: moving from unspoken to outspoken
Bibliographic record
Abstract
Racism is a public health crisis. Black communities (including Africans, the African diaspora and people of African descent) experience worse health outcomes as demonstrated by almost any measure of health and wellbeing-e.g. life expectancy; disease prevalence; maternal mortality rates. While health promotion has its foundation in promoting equity and social justice, it is clear that however well-intended, we are not affecting meaningful change for Black communities quickly enough. Through this article, we outline the intersection of social determinants of health and anti-Black racism. We describe how in the first 8 months of 2020 Black communities around the globe have been disproportionately affected by COVID-19, while also having to respond to new instances of police brutality. We assert that the time has come for health promotion to stop neutralizing the specific needs of Black communities into unspoken 'good intentions'. Instead, we offer some concrete ways for the field to become outspoken, intentional and honest in acknowledging what it will take to radically shift how we promote health and wellbeing for Black people.
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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 | 0.020 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.016 | 0.030 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.001 | 0.019 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 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".