Perception of Web-Based Didactic Activities During the COVID-19 Pandemic Among Anesthesia Residents: Pilot Questionnaire Study
Bibliographic record
Abstract
BACKGROUND: Physical and social distancing recommendations aimed at limiting exposure during the COVID-19 pandemic have forced residency programs to increasingly rely on videoconferencing and web-based resources. OBJECTIVE: In this pilot study, we aimed to explore the effects of the COVID-19 pandemic on residency training experience, and to delineate the perceived barriers to the successful implementation of web-based medical education. METHODS: A 19-item survey was compiled and distributed electronically using Qualtrics. This anonymous survey included information on the training level of each resident, their participation in formal didactics before and during the pandemic, and their perception of the ease and limitations of virtual didactics. The resident's opinions on specific educational resources were assessed, and the effectiveness of new delivery methods on resident engagement and learning was examined. RESULTS: Thirty anesthesiology residents were surveyed, 19 of whom agreed to participate in the pilot study. One participant with incomplete responses was excluded, yielding a final cohort of 18 respondents. Most residents (56%, 10/18) reported that the COVID-19 pandemic negatively affected their residency training. The time spent on didactic training and independent studies was, nevertheless, not affected by the pandemic for 90% (16/18) of respondents. Nonetheless, 72% (13/18) of residents were less engaged during virtual lectures in comparison to in-person didactics. Important limitations included distraction from the physical environment (67%, 12/18), internet instability (67%, 12/18), less obligation to participate (44%, 8/18), technical difficulty and unmuted microphones (33%, 6/18, each), and people speaking over each other (28%, 5/18). Despite these limitations, most residents stated that they would like to keep a combination of virtual didactics including live Zoom lectures (56%, 10/18), prerecorded web didactics (56%, 10/18), and virtual ground rounds via Zoom (50%, 9/18) as the "new normal." CONCLUSIONS: Despite important limitations listed in this report, anesthesia residents would like to keep a combination of virtual lectures and presentations as the new normal after the COVID-19 pandemic.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".