Cultural Sensitivity Training in Canadian Medical Schools
Bibliographic record
Abstract
The authors describe the results of a survey they carried out in 2000 to determine the status of cultural sensitivity training in 16 Canadian medical schools using structured telephone interviews of associate and assistant deans or curriculum directors and curriculum coordinators. Their goal was to obtain a descriptive analysis of school-specific objectives, curriculum content, methods, and evaluation formats. The survey was prompted by the growing concern that in culturally diverse societies, medical education has failed to keep pace with the changing composition of the patient population. Only one of the eight schools that integrated cultural sensitivity within their objectives made explicit mention of the topic in its clerkship evaluation form. While seven of the 16 schools did not have any statement on cultural sensitivity in their curricular objectives, they integrated cultural sensitivity in their curricula using various educational methods, with PBL cases, lectures, and small-group discussions being the commonest formats. These educational methods were primarily offered to students in their first and second years. Student participation was required, but program lengths ranged from two to 40 hours. Additional findings for each school are presented. The authors conclude that while progress has been made, lack of adequate resources and a number of obstacles to inclusion of multicultural health content in curricula appear to remain ongoing problems. Further investment in faculty development and administrative staff support for a multicultural curriculum are needed, as is more research on effective curricular components.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".