Development and Implementation of an International Virtual Didactic Series for Physical Medicine and Rehabilitation Graduate Medical Education During COVID-19
Bibliographic record
Abstract
ABSTRACT: Coronavirus disease of 2019 presented significant challenges to residency and fellowship programs. Didactic lectures were particularly affected as redeployment of faculty and trainees, limitations on in-person gathering, and other barriers limited opportunities for educational engagement. We sought to develop an online didactic series to address this gap in graduate medical education.Lecturers were recruited via convenience sample and from previous Association of Academic Physiatrists presenters from across the United States and Canada; these presented via Zoom during April and May 2020. Lecturers and content reflected the diverse nature of the specialty. Learning objectives were adapted from the list of board examination topics provided by the American Board of Physical Medicine and Rehabilitation.Fifty-nine lectures were presented. Maximum concurrent live viewership totaled 4272 and recorded lecture viewership accounted for an additional 6849 views, for a total of at least 11,208 views between the date of the first lecture (April 9, 2020) and May 1, 2021. Live viewers of one of the lectures reported participating from several states and 16 countries.The Association of Academic Physiatrists-led virtual didactics augmented graduate medical education during the coronavirus disease of 2019 pandemic, and data confirm that the lectures have continued to enjoy a high level of viewership after the cessation of live lectures.
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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".