Bibliographic record
Abstract
While it holds true that visible minorities often benefit less than average from healthcare systems in North America, there is yet to be consensus on the extent to which racism and other institutionalized issues play a role in leaving them at a serious disadvantage. A 2018 study by Dr. Elizabeth Howell reports that African American women face severe maternal morbidity at rates two-fold that of non-Hispanic white women, which in light of the Black Lives Matter movement, brings to question the integrity of the maternal care system and the professionals who work within it. Many health problems faced by Black, Indigenous, and People of Colour (BIPOC) have not only been a result of racial prejudice, but also disparities beyond the control of individual healthcare providers. In his anthology of essays, Disease, Life, and Man, Rudolf Virchow underscores the origins of disease as rather originating from structural flaws in health states dictated by the democratic polity. Although racism contributes greatly to healthcare inequality, significant disparities also stem from socioeconomic barriers that impede minority access to healthcare. The purpose of this article is to examine the institutional disparities influencing health accessibility for BIPOC women, and analyze its effects on the maternal health of racial minorities with an emphasis on Hamilton, Ontario.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.098 | 0.007 |
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".