Dataset of the vascular e-Learning during the COVID-19 pandemic (EL-COVID) survey
Bibliographic record
Abstract
This dataset supports the findings of the vascular e-Learning during the COVID-19 pandemic survey (the EL-COVID survey). The General Data Protection Regulation (GDPR) of the European Union was taken into consideration in all steps of data handling. The survey was approved by the institutional ethics committee of the Primary Investigator and an online English survey consisting of 18 questions was developed ad-hoc. A bilingual English-Mandarin version of the questionnaire was developed according to the instructions of the Chinese Medical Association in order to be used in mainland People's Republic of China. Differences between the two questionnaires were minor and did affect the process of data collection. Both questionnaires were hosted online. The EL-COVID survey was advertised through major social media. All national and regional contributors contacted their respective colleagues through direct messaging on social media or by email. Eight national societies or groups supported the dissemination of the EL-COVID survey. The data provided demographics information of the EL-COVID participants and an insight on the level of difficulty in accessing or citing previously attended online activities and whether participants were keen on citing these activities in their Curricula Vitae. A categorization of additional comments made by the participants are also based on the data. The survey responses were filtered, anonymized and submitted to descriptive analysis of percentage.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".