Training Students to Appraise the Quality of Scientific Literature
Bibliographic record
Abstract
Implementing evidence-based veterinary medicine (EBVM) into clinical practice requires not only the ability to retrieve, interpret, and apply the results of published scientific studies, but also the ability to critically evaluate the quality of the literature. These skills, however, are not widely taught in the veterinary curriculum. The objective of this study was to test a literature evaluation form (LEF) designed to assist veterinary students in appraising the quality of literature on animal reproduction and to compare their ability to do so with that of students who were provided with a control form (CF). The 68 participants were in their fifth year of study and attended a clinical rotation at the Clinic for Animal Reproduction. Students in the LEF group determined the quality of two scientific papers, considering statements about study design, information content, and objectivity, and determined rating points to obtain an overall score. Participants using the CF ranked the quality of the article without the assistance of the quality assessment form. The LEF group was able to more correctly assess the quality of the literature and the variability of the chosen evidence levels was higher in the CF group. The questionnaire was found to be a useful tool for the systematic assessment of the quality of publications within a reasonable period of time. Seventy-eight per cent of the participants agreed that the LEF helps them evaluate the quality and validity of biomedical scientific information. We conclude that courses that introduce EBVM should be taught in the first semesters of the veterinary curriculum so that students can develop competence in defining a clinical problem, retrieving information from the literature, and developing independent critical thinking.
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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 | 0.028 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".