Transforming the Academic Faculty Perspective in Graduate Medical Education to Better Align Educational and Clinical Outcomes
Bibliographic record
Abstract
The current health care delivery model continues to fall short in achieving the desired patient safety and quality-of-care outcomes for patients. And, until recently, an explicit acknowledgment of the role and influence of the clinical learning environment on professional development had been missing from physician-based competency frameworks. In this Perspective, the authors explore the implications of the insufficient integration of education about patient safety and quality improvement by academic faculty into the clinical learning environment in many graduate medical education (GME) programs, and the important role that academic faculty need to play to better align the educational and clinical contexts to improve both learner and patient outcomes. The authors propose a framework that closely aligns the educational and clinical contexts, such that both educational and clinical outcomes are centered around the patient. This will require a reorganization of academic faculty perspective and educational design of GME training programs that recognizes that (1) the dynamic interplay between the faculty, learner, training program, and clinical microsystem ultimately influences the quality of physician that emerges from the training program and environment, and (2) patient outcomes relate to the quality of education and the success of clinical microsystems. To enable this evolution, there is a need to revisit the core competencies expected of academic faculty, implement innovative faculty development strategies, examine closely faculty's current clinical super vision practices, and establish a training environment that supports bridging from clinician to educator, training program to clinical microsystem, and educational outcomes to clinical outcomes that benefit patients.
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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 | 0.045 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.011 | 0.035 |
| Scholarly communication | 0.023 | 0.011 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.004 | 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".