Use of Online Resources to Study Cardiology by Clinical Veterinary Students in the United Kingdom
Bibliographic record
Abstract
Online resources are being increasingly used by veterinary students to complement their learning. However, their use by veterinary students, especially for cardiology learning, remains poorly understood. This article investigates the extent to which clinical veterinary students use online resources to study cardiology and whether this is affected by factors of gender, age, year of study, or entry status. This was a questionnaire-based study distributed to clinical veterinary students across eight UK universities and achieved 213 respondents. The lecturer was the most preferred resource except for direct entry students and students aged 27 or more, who preferred recommended textbooks. Some 95.3% of students use search engines to research cardiology topics, and 93.4% indicated that they would first search for answers online rather than contacting their instructor. Online video clips were popular as 71.8% of students accessed them at least once per week for cardiology learning. Of those students, 89.3% found online videos useful for understanding cardiological concepts. Social media was only rarely used (6.6%) to discuss cardiology information. Nonetheless, most students (64.3%) stated that they would enjoy interacting with course material on an instructor-led social media page. Despite most students (62%) not automatically trusting online resources, only 46.9% of students indicated that they verify online cardiology information. Online resources play an important role in complementing traditional resources in cardiology learning and suggest that some level of academic oversight may be necessary to ensure students use these resources in an appropriate manner.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".