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.This work contributes in important ways to better understanding the considerable variability that exists in residency training, including unpacking what might be warranted or unwarranted variability.The authors' findings 1 are consistent with those of recent publications also using EHR data to characterize the wide variations in clinical content exposure that exist at both the program and individual resident levels. 2,3Their research 1 also adds new dimensions to our understanding of such variation by evaluating additional factors, including patient acuity, complexity, and social determinants of health.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".