Junior Rounds: an educational initiative to improve role transitions for junior residents
Bibliographic record
Abstract
OBJECTIVE: At our institution, Morning Report focuses mostly on diagnostic reasoning. This makes it a challenge for first-year residents to learn to manage common on-call emergencies, such as hyperkalemia. We sought to improve their preparedness for the transitions they would encounter: from medical student to physician at the beginning of the academic year, and from junior resident to senior resident toward the end. In response to feedback, we developed the Junior Rounds curriculum: a weekly session focused on the approach to commonly encountered on-call emergencies and internal medicine referrals. Anonymous surveys were sent to trainees, and iterative analysis of monthly feedback led to changes to Junior Rounds. RESULTS: Junior Rounds was implemented from August 2015 to June 2016. Thirty-nine of 92 possible respondents (44%) completed surveys in that period. Most respondents agreed that Junior Rounds met their educational needs, was presented at an appropriate level, and was more important to their learning than other available educational activities. Our experience demonstrates that dedicated time for level-specific learning aimed to support the transitions of junior residents can be successfully achieved. Iterative adjustment to these rounds based on feedback allowed for evolution of the curriculum to meet the changing priorities of junior learners.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".