From Spark to Flame -- Radical Innovations from Cataclysmic Events in Medical Education
Bibliographic record
Abstract
This article was migrated. The article was marked as recommended. We all knew it was coming. We just didn't realize it would all come at once. No, we are not talking about the zombie apocalypse, but rather the emergence of virtual teaching and virtual healthcare delivery pervading every aspect of life as we now know it. In the context of COVID-19 and marked shifts in how and where we teach medical learners, the staggering number of new ideas, adaptations, and innovations has been inspiring. This game-changing pandemic is a spark, a lightning bolt if you will, that has created solutions, where previous barriers may have been in virtual teaching and healthcare provision. It is impossible to even consider going "back to normal", as they say. We believe the torrent of ideas and possibilities for medical education, brought by COVID-19, cannot and should not be stopped. We explore the nuances of virtual teaching and virtual care and seek readers to consider what their actionable frameshift can mean for medical education in their teaching realm moving forward. We believe that this is the time to innovate: the time to radically change our traditional medical education practices. To sustain these innovations, institutional support, participant buy-in, and assessment and outcome data will be invaluable to harness these new opportunities.
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 | 0.019 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.049 |
| Scholarly communication | 0.025 | 0.023 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".