The Climate Impact of Medical Residency Interview Travel in the United States and Canada: A Scoping Review
Bibliographic record
Abstract
Background The change from in-person to virtual interviews for graduate medical education (GME) provides the opportunity to compare the potential environmental effects. Objective To explore and summarize the existing literature on the potential climate impact of medical residency interview travel through a scoping review. Methods The search was conducted in October 2022 using 5 research databases. Results were screened for inclusion by 2 reviewers in a 2-tiered process. Inclusion criteria were limited to English language articles from the United States and Canada, with no limitations on the type of study, type of applicant (allopathic, osteopathic, or international medical graduate), or type of residency. A thematic analysis focusing on the objectives and main findings of identified studies was conducted and an iteratively created standardized data extraction worksheet was used such that all studies were explicitly assessed for the presence of the same themes. Results The search identified 1480 unique articles, of which 16 passed title and abstract screening and 13 were ultimately included following full-text review. There were 3 main themes identified: the carbon footprint of residency travel, stakeholders’ perspectives on virtual interviews, and advocacy for virtual interviews. All 13 articles employed persuasive language on interview reform, ranging from neutral to strongly in favor of virtual interviews based wholly or in part on environmental concerns. Conclusions Two main findings were identified: (1) Though carbon footprint estimates for in-person interviews vary, in-person interviews create considerable carbon emissions and (2) those working in GME are concerned about the climate effects of GME practices and describe them as a compelling reason to permanently adopt virtual interviewing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.100 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.029 | 0.044 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".