Promoting Evidence-based Veterinary Medicine through the online resource ‘EBVM Learning’: User feedback
Bibliographic record
Abstract
Abstract ‘EBVM Learning’ is a freely available resource created in 2015 by an international team with the support of RCVS Knowledge. The resource comprises a series of online modules teaching the fundamental concepts of evidence-based veterinary medicine (EBVM) (Ask, Acquire, Appraise, Apply & Assess) supported by case studies, exercises, worked examples and quizzes. The aim of the current study (undertaken in 2019) was to review ‘EBVM Learning’ to ensure its ongoing relevance and usefulness to the range of learners engaged in EBVM. Feedback was gathered from stakeholder groups using website statistics and feedback forms, a survey and semi-structured interviews to provide a combination of quantitative and qualitative data. Website statistics revealed an international audience and a steady increase in visitors exceeding 1,000 per month in August 2020. Feedback via the online form (n=35) and survey (n=71) indicated that the resource was well structured, with an appropriate level and amount of content, useful examples and quizzes and the majority of respondents would use it again. Semi-structured interviews of educators (n=5) and veterinarians (n=8) identified three themes: features of the ‘EBVM Learning’ resource (strengths, suggestions for improvement), embedding the resource in education (undergraduate, postgraduate) and promoting EBVM (challenges, motivation for engagement). At a project team workshop the results were used to plan updates to the existing content and to identify new ways to promote learning and engagement. An updated version of ‘EBVM Learning’ was developed. ‘EBVM Learning’ is helping to produce the next generation of evidence-based practitioners and enabling to engage in the concepts of EBVM as part of their clinical practice.
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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 | 0.025 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".