How Trainees Become Multimodal Changemakers
Bibliographic record
Abstract
To the Editor: Medical school for many students is a further step to greater changemaking. A new trainee will be exposed to a parade of potential role models, such as leaders in education, accomplished researchers, and those dedicated to clinical and community service. Students are often encouraged to select from the standard leadership areas above. Yet, just as there are many medical specialties, there are many changemaking modalities, and new trainees will benefit from early and broad exposure rather than foreclosing on those first identified or commonly found. Although less frequently mentioned in mainstream medical education, physicians across the country create change through entrepreneurship, holding political office, partnership with industry, and grassroots nonprofits, to name a few. For trainees who find themselves called to make change on issues they feel passionate about, finding the right combination of changemaking modalities can be crucial to both the impact of their efforts and personal satisfaction in their work. Part of this search for a fitting combination involves understanding how one’s personality and life experiences intersect with a given changemaking method. Those who desire maximal autonomy and control over timelines may be better suited to entrepreneurship, while others who desire social policy change may gravitate toward political advocacy. Regardless of whether an explored modality becomes a part of one’s regular repertoire of methods for changemaking, such exploration can give trainees new mental models, problem-solving techniques, and professional networks that will be invaluable for future endeavors. Within increasingly complex health care systems, multimodal avenues of changemaking synergize to increase opportunities for collaboration, personal growth, and satisfaction. To fully unlock the potential of multimodal changemaking, trainees should appreciate the impact of traditionally endorsed methods and also acknowledge the existing bias within the hidden curriculum of medical education. This provides a foundation for open-minded exploration of less common changemaking modalities that can be explored through identifying local mentors in the field or reaching for inspiration outside of medicine. Finally, trainees should reflect on how any given project can be better supported with different modalities of changemaking and which modalities better empower their own strengths and skills. This paradigm can better position a trainee to use their medical education to tackle ever-evolving problems that will require new solutions in the decades to come. Acknowledgments: The authors would like to thank the amazing mentors in changemaking they have had the benefit of learning from during their time as medical students at the University of British Columbia.
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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 | 0.008 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.010 | 0.018 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".