Why Mentorship Matters: The 2023 Trainee-Authored Letters to the Editor
Bibliographic record
Abstract
To the Editor: Since 2016, Academic Medicine’s calls for Trainee-Authored Letters to the Editor have provided trainees across levels and disciplines with opportunities to share their views on pertinent issues in health professions education. Of the 2,646 submissions received through 2023, 408 letters have been accepted from students who plan to enter health professions, students enrolled in health professions schools, residents, fellows, PhD students, and postdoctoral scholars. Taken together, these letters offer meaningful, and often moving, accounts that convey trainees’ experiences and values. These letters also provide insights regarding the nature of training in the health professions, as it is and as it could be. In 2023, the 8th and most recent call focused on mentorship. Two trainee members of Academic Medicine’s editorial team—one of us (D.K.K., a third-year medical student, University of Toledo College of Medicine and Life Sciences) and Joseph R. Geraghty, MD, PhD (a neurology resident, University of Pennsylvania Perelman School of Medicine)—developed the call’s prompt asking “why mentorship has mattered in your professional journey.” Learners were encouraged to reflect on the ways a good (or even a disappointing) mentorship experience shaped their self-understanding, identity, or growth as a professional.1 We received 361 submissions from trainees in the United States and Canada, as well as Australia, Brazil, India, Mexico, the Netherlands, Qatar, Singapore, Switzerland, Uganda, the United Kingdom, Vietnam, and more. These submissions were reviewed by a team of 106 reviewers, including health professions trainees who authored previously published letters; Academic Medicine editorial board members, assistant and associate editors, staff editors, and expert reviewers; MedEdPORTAL faculty mentors; and Association of American Medical Colleges staff members. Each submission was evaluated by 4 reviewers, including 1 trainee, resulting in 1,444 reviews conducted. Academic Medicine’s editorial team and journal staff used the reviewer ratings to select letters for publication. The 89 authors of the 66 accepted letters include 3 undergraduate students, 47 medical students from MD- and DO-granting medical schools, 4 MD-PhD students, 24 residents, 10 fellows, and 1 nursing graduate student. In addition, 54 submissions received honorable mention; their authors are recognized on Academic Medicine’s website (https://journals.lww.com/academicmedicine/Pages/2023-Honorable-Mentions.aspx). We hope these 66 letters will inspire Academic Medicine readers to reflect on the importance of their own mentorship experiences and to reaffirm their dedication to mentoring relationships. Dilpreet K. KaeleyAssistant editor for trainee engagement, Academic MedicineLaura BlytonStaff editor, Academic Medicine
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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 | 0.018 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.007 | 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".