A carbon footprint study of the Canadian medical residency interview tour
Bibliographic record
Abstract
Each spring, thousands of Canadian medical students travel across the country to interview for residency positions, a process known as the CaRMS tour. Despite the large scale of travel, the CaRMS tour has received little environmental scrutiny. To estimate the national carbon footprint of flights associated with the CaRMS tour, as well as reductions in emissions achievable by transitioning to alternative models. We developed a three-question online commuter survey to collect the unique travel itineraries of applicants in the 2020 CaRMS tour. We calculated the emissions associated with all flights and modelled expected emissions for two alternative in-person interview models, and two virtual interview models. We collected 960 responses out of 2943 applicants across all 17 Canadian medical schools. We calculated the carbon footprint of flights for the 2020 CaRMS as 4239 tCO<sub>2</sub>e (tonnes of carbon dioxide equivalents), averaging 1.44 tCO<sub>2</sub>e per applicant. The average applicant’s tour emissions represent 35.1% of the average Canadian’s annual household carbon footprint, and the emissions of 26.7% of respondents exceeded their entire annual ‘2050 carbon budget.’ Centralized in-person interviews could reduce emissions by 13.7% to 74.7%, and virtual interviews by at least 98.4% to 99.9%. Mandatory in-person residency interviews in Canada contribute significant emissions and reflect a culture of emissions-intensive practices. Considerable decarbonization of the CaRMS tour is possible, and transitioning to virtual interviews could eliminate the footprint almost entirely.
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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 | 0.000 | 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) | 0.278 | 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".