Cultural Competence Springs up in the Desert
Bibliographic record
Abstract
The authors describe the factors that led Weill Cornell Medical College in Qatar (WCMC-Q) to establish the Center for Cultural Competence in Health Care from the ground up, and they explore challenges and successes in implementing cultural competence training.Qatar's capital, Doha, is an extremely high-density multicultural setting. When WCMC-Q's first class of medical students began their clinical clerkships at the affiliated teaching hospital Hamad Medical Corporation in 2006, the complicated nature of training in a multicultural and multilingual setting became apparent immediately. In response, initiatives to improve students' cultural competence were undertaken. Initiatives included launching a medical interpretation program in 2007; surveying the patients' spoken languages, examining the effect of an orientation program on interpretation requests, and surveying faculty using the Tool for Assessing Cultural Competence Training in 2008; implementing cultural competence training for students and securing research funding in 2009; and expanding awareness to the Qatar community in 2010. These types of initiatives, which are generally highly valued in U.S. and Canadian settings, are also apropos in the Arabian Gulf region.The authors report on their initial efforts, which can serve as a resource for other programs in the Arabian Gulf region.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.015 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".