Across borders: thoughts and considerations about cultural preservation among immigrant clinicians
Bibliographic record
Abstract
Immigrant clinicians make up 20-28% of the health workforce in many high-income countries, including Australia, Britain, Canada and the USA. Yet, the preserved culture of immigrant clinicians remains largely invisible in the medical literature and discourse. Research on immigrant clinicians primarily attends to medical professional requirements for the adopted country (medical board examination eligibility, fellowship training and licensing). Cultural preservation among immigrant clinicians has not been adequately considered or studied. This paper highlights this notable gap in healthcare delivery and health services research relevant to immigrant clinicians. We propose it is worthwhile to explore possible relationships between immigrant clinicians' preserved culture and clinical practices and outcomes since immigrant clinicians cross borders with their academic training as well as their culture. The sparse literature regarding immigrant clinicians suggests culture influences health beliefs, attitudes about the meaning of illness and clinical practice decisions. Additionally, immigrant clinicians are more likely to serve rural, low-income populations; communities with high density of ethnic minorities and immigrants; and areas with primary care shortage. Therefore, cultural preservation among immigrant clinicians may have important implications for public health and health disparities. This area of inquiry is important, if not urgent, in health services research.
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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 | 0.020 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.023 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".