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.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".