First Experiences: An Introduction to This Year's Trainee-Authored Letters to the Editor
Notice bibliographique
Résumé
“I had a great day—I came up with the plan for a patient for the first time!” “And I wrote the entire note on my own!” “I just delivered my first baby!!!” The inspiration for Academic Medicine’s 2019 call for letters to the editor1 from learners came from texts received by one of us (L.W.R.) from a third-year medical student mentee at another medical school who wrote last summer to celebrate some “first” moments on his journey to becoming a physician. The invitation for letters was our journal’s fourth annual call devoted to trainees—and, to our astonishment, we received over 400 submissions—more than twice the number we received in 2018 (on trust) and 2017 (on transitions) and 4 times the number we received in 2016, the first year of the call. The response delighted us—and it also caused some logistical scrambling! We were absolutely committed to providing a fair evaluative process involving multiple reviewers for each and every letter. We knew that identifying the reviewers and then coordinating the review process in a compressed time frame could prove challenging, but we mobilized our supportive community. Individual letters were reviewed initially by teams of editorial board members, associate editors and staff editors of the journal; journal reviewers; and rising scholars in academic medicine. Then, every letter and the recommendations by the reviewer teams were carefully evaluated for publication by the editor-in-chief (L.W.R.). One reviewer noted that reading the letters was among the most satisfying tasks he has ever done in his professional career, and we agree. We believe that reviewing the letters is so rewarding because the voices are so fresh and because the authors’ stories are often so raw. The letters provide a glimpse into who our future physicians will be and inspire a hard look at who we medical educators are now. The authors show vulnerability (revealing errors, disappointments, biases), humility (expressing profound respect for patients and awe at patients’ endurance and generosity), growth (from fear to confidence), wisdom (understanding that healing is not always about solving medical problems), energy and initiative (not just identifying but actually taking steps to address problems), and perceptiveness (highlighting gaps in training programs). Reading the letters is enlightening and moving, accompanied for us by moist eyes at times and belly laughs at others. The stories, insights, and lessons they convey made selecting just a handful all the more difficult. For this reason, we did our best to develop a slate of accepted letters that would represent a variety of themes and a diversity of authors. We wanted the topics and themes of the final selected letters to be representative of all the letters we received (burnout, errors, anatomy class), but we also wanted to showcase unique topics (delivering a baby—your own!—on the interstate), as well as matters of vital importance (female genital mutilation). The authors whose letters appear in the pages of Academic Medicine represent learners at all levels—fellows, residents, medical students, and even one premed/baccalaureate student—and they represent learners in private and public institutions from across the United States, Canada, and abroad. We acknowledge that there were more beautifully written letters than we had time or space to edit and publish. In the end, only 11% of submissions were accepted for publication. To show our regard for the great work, thoughtfulness, and efforts of authors whose letters received excellent reviews, we have posted a list of honorable mentions on our website (https://journals.lww.com/academicmedicine/Pages/2019-Honorable-Mentions.aspx). We invited those letter writers to share the link with faculty, mentors, residency program directors, and others. A new practice by the journal, we hope that this step has communicated to our authors our tremendous appreciation for the privilege of reading their wonderful narratives. We are thankful to the many learners who shared their stories, and we are excited to showcase 45 letters in the pages of our journal over the next few issues. Laura Weiss Roberts, MD, MAEditor-in-chief, Academic Medicine.Elizabeth S. Karlin, MASenior staff 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 | 0,009 | 0,040 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,009 | 0,002 |
| Communication savante | 0,013 | 0,008 |
| Science ouverte | 0,003 | 0,006 |
| Intégrité de la recherche | 0,008 | 0,012 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,016 | 0,008 |
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 ».