TRASH-CAN: An Approach to Promote Planetary Health Education and Research for Health Care Trainees
Bibliographic record
Abstract
Background As future health care leaders who work and train in diverse clinical settings, resident physicians are uniquely positioned to advance sustainable health care systems. However, residents are insufficiently educated about health care sustainability and given limited opportunities to engage in planetary health. Objective This article introduces and reports on the early outcomes of the Trainee-Led Research and Audit for Sustainability in Healthcare Canada (TRASH-CAN), a resident-driven initiative launched in 2023 with the aim of reducing Canadian health care’s environmental impact. Methods In 2023-2024, we developed a web-based platform that facilitates trainee-led action to support the promotion of sustainability literature, collaboration with national and international institutions, and execution of quality improvement projects to reduce health care waste under the 3 brand pillars of Learning, Leadership, and Delivery. We have promoted TRASH-CAN and its website through conference presentations, social media, mailing lists, and word of mouth. These activities support our goals of engaging trainees, pairing them with mentors, and initiating a variety of quality improvement projects focused on planetary health. Results In its first year of operation, TRASH-CAN has developed a fully functional website hosting intake forms and detailing ongoing projects and opportunities. We have enrolled 15 faculty mentors and 16 residents and medical students, with ongoing projects such as transitioning hospitals to reusable alternatives and optimizing procedural custom operating room equipment packs. Conclusions TRASH-CAN’s inaugural year has led to the initiation of 11 sustainability projects and the enrollment of 31 faculty mentors and trainees.
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 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 | 0.003 | 0.000 |
| 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) | 0.000 | 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".