Vascular E-Learning in Mainland China: results of the e-Learning during the COVID-19 pandemic (EL-COVID) study
Bibliographic record
Abstract
Abstract Purpose: With the onset of the COVID 19 pandemic, digitalization came to the forefront of education delivery and continuous professional development took place predominantly online. We investigate the relevance of e-Learning in the vascular surgery community in mainland People’s Republic of China (PRC) and address the regional variability in comparison with the international community Methods: The international EL-COVID survey took place online from June 15, 2020 to October 15, 2020. We subtracted and analyzed the data from the PRC participants. Results: From 84 different countries, PRC had the largest contribution to the EL-COVID study (n = 109, 12.7%). Most of the Chinese responders were experienced vascular surgeons (73.39% vs. 53.81%; p=0.0001) and attended more than four eL activities (52.29% vs. 54.08%; not significant). Female vascular surgeons were underrepresented: 7.33% vs. 23.15%; p=0.0002. While participation at international activities did not vary, attendance at national eL activities was reduced (27.52% vs. 73.62%, p<0.0001). Obtaining official accreditation/CME points was relevant in choosing what eL opportunities to attend. Employers were less supportive of participation during working hours (17.43% vs. 46.52%; p<0,0001). eL opportunities were mainly promoted on social media (44.04% vs. 27.17%; p=0.0003) and to a lesser degree through direct contact from national/international societies (27.52% vs. 39.49%; p=0.016). Conclusion: As in other countries, eL is gaining relevance in the vascular surgery community of PRC. Adequate support as well as improving the dissemination strategy of national societies are needed to meet the demands of the modern vascular surgeon.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".