First Experiences: An Introduction to This Year's Trainee-Authored Letters to the Editor
Bibliographic record
Abstract
“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.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.016 | 0.008 |
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".