A Novel Transition to Practice Curriculum for General Internal Medicine Trainees
Bibliographic record
Abstract
Background: Physicians face numerous challenges during the transition from residency training to independent practice. Residency programs often provide little to no training around the non-clinical aspects of establishing an independent practice. Methods: We designed and implemented a longitudinal transition to practice (TTP) curriculum tailored to the needs of general internal medicine (GIM) trainees. Our curriculum included eleven sessions spread across four themes: “Entering the Workforce,” “Managing Your Practice,” “Managing Your Finances,” and “Maintenance of Wellness.” Results: Eleven residents participated in the curriculum. Most residents agreed or strongly agreed that the curriculum included topics that were important to TTP (91%), that the sessions improved their comfort level with the topics presented (100%), and that the curriculum was an important part of their residency training (91%). Personal finance and wellness sessions were particularly well received. Conclusion: Our longitudinal curriculum for teaching non-clinical TTP competencies was feasible and well-received by GIM trainees. However, further research is needed to establish whether such curricula lead to changes in behavior and outcomes.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".