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Record W2103098186 · doi:10.24908/pceea.v0i0.4700

Description of Technical Electives that Prepare Engineers for Careers in the Medical and Health Care Professions

2012· article· en· W2103098186 on OpenAlexaffvenueabout
Danny Mann, Jacquie Ripat, Art Quanbury, Jason Morrison, Jitendra Paliwal

Bibliographic record

VenueProceedings of the Canadian Engineering Education Association (CEEA) · 2012
Typearticle
Languageen
FieldEngineering
TopicBiomedical and Engineering Education
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsWork (physics)Engineering ethicsHealth professionsHealth careField (mathematics)Medical educationHealth systems engineeringEngineering managementEngineeringMedicineMechanical engineeringPolitical scienceMathematics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.425
Threshold uncertainty score0.421

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.248
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2012
Admission routes3
Has abstractyes

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