Educational Alternatives for the Maintenance of Educational Competencies in Surgical Training Programs Affected by the COVID-19 Pandemic
Bibliographic record
Abstract
Along with the socio-economic burden the COVID-19 pandemic carried, the strain it brought upon our health care system is unparalleled. In an attempt to conserve much needed personal protective equipment (PPE) as well as to free up available hospital beds to accommodate the significant influx of COVID-19 patients, many elective surgical cases were essentially put on hold. Furthermore, to taper the spread of this highly contagious virus and to protect the medical staff, surgical clinics were limited to urgent care that could not be managed through virtual platforms. Surgical trainees, such as residents and fellows, who solemnly rely on clinical and surgical exposure to hone their operative and clinical skills, were evidently left deprived. As the pandemic rapidly progressed, medical staff in the emergency departments and what is now known as the COVID wards and COVID ICUs quickly became overwhelmed and overworked. This new reality required surgical trainees to rapidly redeploy to help meet the rising hospital needs. With no clear end to this pandemic, surgical trainees worry they will not reach the appropriate milestones and acquire the amount of surgical experience required to become competent surgeons. As a result, a rapid solution should be found and applied to remedy this newly created gap in surgical education. The measures we recommend include access to regular webinars from world-renowned experts, increased implementation of surgical simulation, selective redeployment of residents to favor level-appropriate learning opportunities and lastly, the active participation of trainees in telemedicine with an increase in surgical exposure as soon as the restrictions are lifted.
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 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".