Reimagining medical education: Part three – Necessity, change, and innovation in uncertain times
Bibliographic record
Abstract
Change has been the keyword to describe the dramatic and rapid impact that the COVID-19 pandemic of 2020 has had on the medical education system worldwide. As a result, there has been a clear necessity for learners, faculty/teachers, leaders, health-care organizations, and academic institutions to “react,” “pivot,” and “reimagine” the system to address the numerous challenges that we have had not only to face in the short term but also to ensure that functions and processes are ready to be reinitiated to move ahead. The rapidly evolving circumstances of the pandemic have caused considerable uncertainty about almost everything, and as such, it has been difficult to anticipate what the future will be like, let alone the intermediate term. The environment of change and uncertainty has created the opportunity for many to be creative and innovative as they strive to support learners, faculty/teachers, health-care organizations, and academic institutions within the ever-changing reality we have found ourselves within.
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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.047 |
| Scholarly communication | 0.029 | 0.026 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".