Hackathons within medical education: Promoting cutting-edge innovation in surgery
Bibliographic record
Abstract

 
 
 Medical students need to begin to learn how to innovate earlier in their training. Hackathons offer opportunities to foster innovation in healthcare. We launched a hackathon for medical students to generate solutions to a real-world surgical problem. We focused on generating solutions to better support more women in surgery, an area of medicine where women remain underrepresented. The goal of our event was to not only generate solutions at a systemic level but within our own medical school, break down barriers for female medical students by allowing them to network with Dalhousie surgeons and better explore potential career goals by attending the event. Attendees reported the event provided an opportunity to build problem solving skills, communication skills and the opportunity to network with like-minded peers. Our hackathon supported idea generation however further emphasis on translation of solutions from idea generation to implementation within our healthcare system is needed.
 
 
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".