Immigrant, black and racialized people’s health
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
Immigrant, black and racialized people’s health Learn about the research of Dr. Bukola Salami, Professor at Cumming School of Medicine, University of Calgary, in this particular focus on Immigrant, Black, and Racialized People’s Health. Immigrants often arrive in Canada in better health than the Canadian-born population due to pre-arrival health screening. This phenomenon is called the healthy immigrant effect. However, the health of immigrants often declines after a period of time in Canada. Several factors contribute to this health decline, including poor socioeconomic outcomes, healthcare access barriers, and discrimination. Professor Salami’s research program focuses on policies and practices shaping migrant and Black people’s health. She has been involved in over 85 funded studies totaling over $230 million. She has led research projects on topics including African immigrant child health, immigrant mental health, access to healthcare for Black women, access to healthcare for immigrant children, Black youth mental health, the health of internally displaced children, the well-being of temporary foreign workers, COVID-19 vaccine hesitancy among Black Canadians, an environmental scan of equity-seeking organizations in Alberta, culturally appropriate practices for research with Black Canadians, international nurse migration, and parenting practices of African immigrants.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it