What to Do When the World Comes to You: Working with Immigrants in Family Practice
Bibliographic record
Abstract
Over 20% of Canada’s population is foreign born and over one million people are considered new Canadians, having received citizenship within the last five years. 1 Although Canada has always seen large waves of immigrants, in the last 40 years there has been a dramatic increase in immigrants arriving from Asia, Africa, Latin America and the Caribbean. Immigrants may have different risk factor profiles based on genetic risks and different environmental exposures, both pre- and post-migration. Discordance in language and culture may challenge the therapeutic relationship. Given the number of new immigrants in Canada, family physicians become responsible for understanding the nuances of dealing with patients who are foreign-born. The immigrant population is immensely heterogeneous. The majority of immigrants arriving in Canada are accepted as “economic immigrants”. Many have excelled academically, speak multiple languages and may have a job upon arrival. Another 25% of immigrants are sponsored by family members and 10% are refugees.2 The risk factor profile of a university professor from Argentina may differ dramatically from a Burmese woman who has languished in a refugee camp for decades. Despite such heterogeneity, there are some commonalities in health risks based on countries of origin and migration histories. For example, there are higher rates of hemoglobinopathies in people of African descent and refugees often have higher risks for infectious disease. This module identifies approaches to specific issues that challenge family physicians caring for immigrant populations.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.012 |
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".