Characterizing Residents’ Clinical Experiences—A Step Toward Precision Education
Bibliographic record
Abstract
Experiential learning from patient care activities is the primary means by which residents develop the skills that shape their future practice.However, these clinical learning opportunities are typically not well characterized, limiting educators' ability to precisely tailor the experiential curriculum.Elsewhere in JAMA Network Open, Lam and colleagues 1 provide a detailed characterization of internal medicine residents' experiences admitting patients overnight within a single Canadian residency program's 5 teaching hospitals over a 10-year period.The authors developed a bespoke clinical-educational database that captures rich data from the electronic health record (EHR) for each included clinical encounter and subsequently attributes each to a specific admitting resident.Leveraging this database, they identified significant resident, hospital, and temporal variation across 6 domains, including both admission volumes and multiple patient and encounter characteristics.
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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 | 0.044 | 0.117 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".