Environmental Impact of Online Versus in-Person Critical Care Education Through the Carbon Footprint Analysis of the CERTAIN Program: Cross-Sectional Study
Bibliographic record
Abstract
Background: Climate change is a pressing public health issue, with the US health care sector contributing about 479 million tons of carbon dioxide (CO2) annually. Online continuing medical education offers an alternative solution to increase global education delivery while reducing CO2 emissions associated with traditional teaching methods. Objective: This study aimed to evaluate the carbon dioxide equivalent (CO2e) emissions associated with different delivery methods of the CERTAIN (Checklist for Early Recognition and Treatment of Acute Illness and Injury) global critical care education program. Specifically, we aimed to compare the climate impact of local in-person courses in Rochester, MN, international in-person courses, and online courses to determine the potential environmental benefits of transitioning to digital education platforms. Methods: A cross-sectional analysis of CO2e emissions linked to the CERTAIN program was conducted from 2016 to 2022. We compared the climate impact of 3 different course offerings: local in-person at Rochester, MN, international in-person courses, and online courses. The international conferences were conducted in the host country with faculty traveling there to provide the educational content. CO2e emissions were calculated using the "My Climate Flight Calculator" and "Environmental Protection Agency Emission Factors" formulas for travel, conference venues, and online course-related emissions. Learner satisfaction was assessed via validated 5-point Likert surveys. Results: Local courses had the highest emissions: 52.7 tons/course (2.5 tons/participant), 96% from air travel (50.6tons, P<.001), versus other formats. International courses showed 20.2 tons/course (0.4 tons/participant), of which 93%(18.8 tons) were travel-related. Online courses reduced emissions by 96% per capita (0.1 tons/participant, P<.001) and 89% per course (5.6 tons, P<.001) versus local format. Overall course ratings were either excellent (live 50%, n=136) vs online 44%, n=11) or very good (live 30.9%, n=84 vs online 53%, n=12) for both live and online courses. Conclusions: The transition to online delivery of our CERTAIN global education program has led to a substantial reduction in CO2 emissions, mainly by eliminating travel, with similar levels of learner satisfaction. These findings support a strategic shift toward digital medical education platforms to promote environmental responsibility and broaden global educational access.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".