Systems-Based Practice in Graduate Medical Education: Evolving Toward an Ideal Future State
Bibliographic record
Abstract
ABSTRACT: Systems-based practice (SBP) was first introduced as a core competency in graduate medical education (GME) in 2002 by the Accreditation Council for Graduate Medical Education as part of the Outcomes Project. While inclusion of SBP content in GME has become increasingly common, there have also been well-documented stumbling blocks, including perceptions that SBP has eroded the amount of curricular time available for more medically focused competencies, is not relevant for some practice contexts, and is not introduced early enough in training. As a result, SBP learning experiences often feel disconnected from medical trainees' practical reality. In this commentary, the authors provide guidance regarding potential changes that may facilitate the evolution of SBP toward an ideal future state where graduates bring a systems science mindset to all aspects of their work. Specific suggestions include the following: (1) expanding the SBP toolbox to reflect current-day health system needs, (2) evolve the teaching methodology, (3) broadening the scope of relevant SBP content areas, and (4) emphasizing SBP as an integrated responsibility for all health care team members. Levers to enact this transformation exist and must be used to influence change at the learner, faculty, program, and clinical learning environment levels.Physicians operate within an increasingly complex health care system that highlights the intersection of health care with complex social, environmental, and relational contexts. Consequently, the role of SBP in both physician work responsibilities and educational requirements continues to expand. To meet this growing demand, GME must adapt how it supports and trains the next generation of systems thinkers, ensuring they understand how levers in the health care system directly affect health outcomes for their patients, and integrate SBP into the foundation of GME curricula in an inclusive, holistic, and unrestrained way.
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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 | 0.026 | 0.045 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.026 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.017 | 0.020 |
| 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".