Evaluation of the Attitudes and Opinions of Veterinary School Students on Distance Education During the COVID-19 Pandemic
Bibliographic record
Abstract
This study aimed to determine the attitudes and opinions of the students of veterinary schools in Turkey regarding distance education during the COVID-19 pandemic. The study was conducted in two stages: (1) to develop and validate a scale for assessing Turkish veterinary students’ attitudes and opinions regarding distance education (DE) ( n = 250 students; one veterinary school) and (2) widespread use of this scale amongst veterinary students ( n = 1,599 students, 19 veterinary schools). Stage 2 was conducted between December 2020 and January 2021 with students from years 2, 3, 4, and 5 who had experienced face-to-face and distance education. The scale contained 38 questions, which were divided into seven sub-factors. Most students considered that practical courses (77.1%) should not continue to be delivered by DE and that catch-up face-to-face programs (77%) would be required for practical skills after the pandemic. The main benefits of DE were that studies did not have to be interrupted (53.2%) and the ability to retrieve online video material for later study (81.2%). A total of 69% of students considered DE systems and applications easy to use. Many (71%) students considered that the use of DE would adversely affect their professional skills, 26.5% expected that the duration of their studies would be extended, but only 18.1% had considered suspending their studies for the period of the pandemic. Therefore, it appeared that face-to-face education was considered indispensable by students in veterinary schools, which provides practice-oriented education in the field of health sciences. However, the DE method can be used as a supplementary tool.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".