Global “systemness” in medical education: A rationale and framework to assess performance
Bibliographic record
Abstract
Healthcare is global. The challenges of the "triple aim" - achieving high-quality healthcare, maximal value, and an excellent patient experience and outcomes - are universal. Medical education is similarly global with worldwide efforts towards competency-based reform, the adoption and adaptation of accreditation standards, and the expansion of international collaborations between healthcare organizations (HCOs). The focus of many of these efforts centers around recognizing education as a talent pipeline to serve local and global healthcare needs. Accordingly, many U.S.-based academic medical centres are pursuing an increasingly global footprint by developing international partnerships between HCOs. The educational leadership at the Cleveland Clinic (an HCO that has ventured internationally in Canada, the United Kingdom, and the United Arab Emirates) has adopted a "systemness" approach to medical education collaboratives. Systemness describes the ability of academic health systems to leverage existing structures, expertise, and other resources to address broadly shared educational needs across geographies, disseminate best practices, and ultimately improve the care that is delivered. The rationale for systemness, a concept derived from the healthcare administration and business world, affords the opportunity to achieve educational outcomes through synergy that exceeds the capability of any single component of a system. In this perspective, we posit a "systemness" taxonomy to be used to assess the performance and success of international collaborations in medical education and provide examples of its application to existing international partnerships in medical education. This framework is grounded in developmental assessment approaches, akin to those used in assessing learner performance, and defines levels of educational collaboration proficiencies, ultimately towards the alignment of these efforts with the health needs of the communities they serve. As global medical education collaboratives advance, ongoing assessment of existing partnerships and further research will be needed to define competencies and integrative activities that define high-performing medical education partnerships.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".