(In)Visible Minorities in Canadian Health Data and Research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This synthesis project is motivated by the apparent neglect of visible minorities in Canadian health data and research. The main question is: Are visible minorities invisible in Canadian health data and research? To address this question, we assess the nature, extent, and range of data and research available on the health and health care access of visible minorities in Canada. Specifically, we summarize: (1) mortality and morbidity patterns for visible minorities; (2) determinants of visible minority health; (3) health status and determinants of visible minority older adult (VMOA) health; and (4) promising data sources that may be used to examine visible minority health in future research.\nWhile we reviewed a large number of publications, we note that only 5 examined population-‐level data to specifically compare visible minorities with white Canadians and just 2 distinguished between Canadian-‐born visible minorities and foreign-‐born visible minorities. In addition, because of data and methodological limitations, and differences in topics examined, findings are not easily comparable to provide a clear picture of visible minorities’ health.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it