Student and alumni perspectives on paths into and out of biomedical engineering
Bibliographic record
Abstract
Biomedical engineering (BME) has one of the highest proportion of women students of any discipline in Canada, and at University of British Columbia (UBC). Anecdotal evidence, based on instructors' interactions with senior students, suggests that women and gender-minority students in UBC’s BME program might leave engineering post-graduation, at higher numbers and for careers in health sciences/medicine. Our aim was to examine reasons for choosing BME, changes in career goals degree and post-graduation career paths of alumni. We conducted 21 semi-structured interviews with current/past BME students and identified five themes related to pursuing/continuing in BME: Positive and Negative Family Influences; Safety Net of a BME Degree; Dual Identities as Engineer and Physician; Perceived Need for High Grade Averages as a Barrier; and Social Environment as a Factor in Choosing Disciplines. These themes are interconnected, and a combination of themes is needed to explain why students choose to pursue/continue in BME.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.004 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".