A chance for reform: the environmental impact of travel for general surgery residency interviews
Bibliographic record
Abstract
Background: In light of the global climate emergency, it is worth reconsidering the current practice of medical students traveling to interview for residency positions. We sought to estimate carbon dioxide (CO2) emissions associated with travel for general surgery residency interviews in Canada, and the potential avoided emissions if interviews were restructured. Methods: An 8-item survey was constructed to collect data on cities visited, travel modalities, and costs incurred. Applicants to the University of Ottawa General Surgery Program during the 2019/20 Canadian Resident Matching Service (CaRMS) cycle were invited to complete the survey. Potential reductions in CO2 emissions were modeled using a regionalized interview process with either one or two cities. Results: Of a total of 56 applicants, 39 (70%) completed the survey. Applicants on average visited 10 cities with a mean total cost of $4,866 (95% CI=3,995-5,737) per applicant. Mean CO2 emissions were 1.82 (95% CI=1.50-2.14) tonnes per applicant, and the total CO2 emissions by applicants was estimated to be 101.9 (95% CI=84.0 – 119.8) tonnes. In models wherein interviews are regionalized to one or two cities, emissions would be 57.9 tonnes (43.2% reduction) and 84.2 tonnes (17.4% reduction), respectively. Overall, 74.4% of respondents were concerned about the environmental impact of travel and 46% would prefer to interview by videoconference. Conclusion: Travel for general surgery residency interviews in Canada is associated with a considerable environmental impact. These findings are likely generalizable to other residency programs. Given the global climate crisis, the CaRMS application process must consider alternative structures.
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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 | 0.019 | 0.073 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 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".