Virtual learning during the COVID-19 pandemic: a turning point in neurosurgical education
Bibliographic record
Abstract
OBJECTIVE: The coronavirus disease 2019 (COVID-19) pandemic has caused dramatic changes in medical education. Social distancing policies have resulted in the rapid adoption of virtual learning (VL) by neurosurgeons as a method to exchange knowledge, but it has been met with variable acceptance. The authors surveyed neurosurgeons from around the world regarding their opinions about VL and how they see the future of neurosurgical conferences. METHODS: The authors conducted a global online survey assessing the experience of neurosurgeons and trainees with VL activities. They also questioned respondents about how they see the future of on-site conferences and scientific meetings. They analyzed responses against demographic data, regions in which the respondents practice, and socioeconomic factors by using frequency histograms and multivariate logistic regression models. RESULTS: Eight hundred ninety-one responses from 96 countries were received. There has been an increase in VL activities since the start of the COVID-19 pandemic. Most respondents perceive this type of learning as positive. Respondents from lower-income nations and regions such as Europe and Central Asia were more receptive to these changes and wanted to see further movement of educational activities (conferences and scientific meetings) into a VL format. The latter desire may be driven by financial savings from not traveling. Most queried neurosurgeons indicated that virtual events are likely to partially replace on-site events. CONCLUSIONS: The pandemic has improved perceptions of VL, and despite its limitations, VL has been well received by the majority of neurosurgeons. Lower-income nations in particular are embracing this technology. VL is still evolving, but its integration with traditional in-person meetings seems inevitable.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".