The education passport: connecting programmatic assessment across learning and practice
Bibliographic record
Abstract
learning over time requires collaboration, cooperation, and trust among learners, regulators, and the public that transcends each individual phase. The authors introduce the concept of an "Education Passport" that provides evidence of readiness to travel across the boundaries between undergraduate medical education, graduate medical education, and the expanse of practice. The Education Passport uses programmatic assessment, a process of collecting numerous low stakes assessments from multiple sources over time, judging these data using criterion-referencing, and enhancing this with coaching and competency committees to understand, process, and accelerate growth without end. Information in the Passport is housed on a cloud-based server controlled by the student/physician over the course of training and practice. These data are mapped to various educational frameworks such Entrustable Professional Activities or milestones for ease of longitudinal performance tracking. At each stage of education and practice the student/physician grants Passport access to all entities that can provide data on performance. Database managers use learning analytics to connect and display information over time that are then used by the student/physician, their assigned or chosen coaches, and review committees to maintain or improve performance. Global information is also collected and analyzed to improve the entire system of learning and care. Developing a true continuum that embraces performance and growth will be a long-term adaptive challenge across many organizations and jurisdictions and will require coordination from regulatory and national agencies. An Education Passport could also serve as an organizing tool and will require research and high-value communication strategies to maximize public trust in the work.
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 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 | 0.032 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.006 |
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".