Why Mentorship Matters: The 2023 Trainee-Authored Letters to the Editor
Notice bibliographique
Résumé
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
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,018 | 0,142 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,004 |
| Communication savante | 0,011 | 0,005 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,017 | 0,016 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,007 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».