The mitigated carbon emissions of transitioning to virtual medical school and residency interviews: A survey‐based study
Bibliographic record
Abstract
Abstract Purpose Prior to COVID, thousands of medical school and residency applicants traversed their countries for in‐person interviews each year. However, data on the greenhouse gas emissions from in‐person interviews is limited. This study estimated greenhouse gas emissions associated with in‐person medical school and residency interviews and explored applicant interview structure preferences. Methods From March to June 2022, we developed and distributed a nine‐question, website‐based survey to collect information on applicant virtual interview schedule, demographics and preference for future interview format. We calculated theoretical emissions for all interviews requiring air travel and performed a content analysis of interview preference explanations. Results We received responses from 258 first‐year and 253 fourth‐year medical students at 26 allopathic US medical schools who interviewed virtually in 2020–2021 and 2021–2022, respectively. Residency applicants participating in the study were interviewed at a mean of 15.3 programs (SD 5.4) and had mean theoretical emissions of 4.31 tons CO 2 eq. Medical school applicants participating in the study were interviewed at a mean of 6.9 programs and had mean theoretical emissions of 2.19 tons CO 2 eq. Ninety percent of medical school applicants and 91% of residency applicants participating in the study expressed a preference for hybrid or virtual interviews going forward. Conclusion In‐person medical training interviews have significant greenhouse gas emissions. Virtual and hybrid alternatives have a high degree of acceptability among applicants.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".