Wilderness Medicine Physician Education: How an Elective Can Spark a Fire
Bibliographic record
Abstract
Background Wilderness medicine (WM) electives offer an opportunity for medical trainees to learn an additional skillset outside of the traditional medical education curricula. Prior literature has yet to detail how participation in WM electives during medical training informs future training (i.e., master's degree, fellowship) or career involvement in the field. Methodology A 25-question survey was completed by former participants of 25 WM electives based in the United States. Survey questions focused on the demographics, motivations, current involvement, and additional WM training among those who participated in WM electives. The survey was completed by 102 eligible participants. Results Of the 102 participants, 53% had been engaged with WM since completing their elective; 18% of the participants had completed additional formal training in WM (i.e., master's degree, fellowship). Further, 95% of participants felt that the elective enhanced their resilience and critical thinking. Of those currently most involved in WM (n = 26), half (46%) were unsure about integrating WM into their careers prior to their elective. Among the uncertain yet highly engaged cohort, 98% cited the elective as the reason they stayed involved in WM. Conclusions These findings underscore the importance of WM electives in fostering interest among medical trainees in WM, and suggest that participation in WM electives may promote further involvement after medical school and residency.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.002 |
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".