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Record W4394766886 · doi:10.1097/acm.0000000000005712

Why Mentorship Matters: The 2023 Trainee-Authored Letters to the Editor

2024· letter· en· W4394766886 on OpenAlexaffabout
Dilpreet K. Kaeley, Laura Blyton

Bibliographic record

VenueAcademic Medicine · 2024
Typeletter
Languageen
FieldMedicine
TopicHealth and Medical Research Impacts
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsMentorshipMedical educationPsychologyMedicine

Abstract

fetched live from OpenAlex

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

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.018
metaresearch head score (Gemma)0.142
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Incentives · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.982
Threshold uncertainty score0.097

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.142
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.004
Scholarly communication0.0110.005
Open science0.0030.002
Research integrity0.0170.016
Insufficient payload (model declined to judge)0.0070.004

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.

Opus teacher head0.140
GPT teacher head0.425
Teacher spread0.285 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainIncentives
GenreCommentary

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".

Quick stats

Citations1
Published2024
Admission routes2
Has abstractyes

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