The Current Status of Medical Genetics Instruction in U.S. and Canadian Medical Schools
Bibliographic record
Abstract
PURPOSE: Relatively little is known about how medical genetics is being taught in the undergraduate medical curriculum and whether educators concur regarding topical priority. This study sought to document the current state of medical genetics education in U.S. and Canadian accredited medical schools. METHOD: In August 2004, surveys were sent from the Indiana University School of Medicine to 149 U.S. and Canadian medical genetics course directors or curricular deans. Returned surveys were collected through June 2005. Participants were asked about material covered, number of contact hours, year in which the course was offered, and what department sponsored the course. Data were collated according to instructional method and course content. RESULTS: The response rate was 75.2%. Most respondents (77%) taught medical genetics in the first year of medical school; only half (47%) reported that medical genetics was incorporated into the third and fourth years. About two thirds of respondents (62%) devoted 20 to 40 hours to medical genetics instruction, which was largely concerned with general concepts (86%) rather than practical application (11%). Forty-six percent of respondents reported teaching a stand-alone course versus 54% who integrated medical genetics into another course. Topics most commonly taught were cancer genetics (94.2%), multifactorial inheritance (91.3%), Mendelian disorders (90.3%), clinical cytogenetics (89.3%), and patterns of inheritance (87.4%). CONCLUSIONS: The findings provide important baseline data relative to guidelines recently established by the Association of American Medical Colleges. Ultimately, improved genetics curricula will help train physicians who are knowledgeable and comfortable discussing and answering questions about genetics with their patients.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".