Description of Technical Electives that Prepare Engineers for Careers in the Medical and Health Care Professions
Bibliographic record
Abstract
In response to student interest in the broad field of biomedical engineering, theDepartment of Biosystems Engineering has developed three technical electives that now form the core of a Biomedical Specialization that is available to students in the Biosystems Engineering program at the University of Manitoba. These courses have been designed to help prepare engineers to meet the challenges of interacting with the medical and health professions. Required courses covering cell biology and physiology provide engineers with a fundamental understanding of living organisms. One cannot interact with medical and health professionals without this basic level of knowledge, however, this basic knowledge is not sufficient. To effectively work with medical and health professionals, engineers must understand the structure of these professions and the roles traditionally played by engineers in these professions. The paper will provide an in-depth description of these three courses. Students completing these courses are well prepared to work in the medical and health professions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".