The Effects of Content Delivery Methods on Ultrasound Knobology and Image Quality Recognition Training in First-Year Veterinary Students
Bibliographic record
Abstract
As new technologies are incorporated into the practice of veterinary medicine, it is imperative we utilize the most effective and impactful content delivery methods. Ultrasound technology has become more affordable and compact for veterinary hospitals, leading to easier incorporation into practice. This study compares three methods of delivering ultrasound knobology content to first-year veterinary students at Texas A&M University College of Veterinary Medicine and Biomedical Sciences. In a prospective study, first-year veterinary students were randomly selected to receive one of three content delivery methods: self-directed active learning (SDL), in-person instructor demonstration, or online module instruction. Knowledge acquisition was assessed using a 10-question quiz for short-term understanding followed by a 10-question quiz after a 6-week period to assess long-term knowledge retention. Student demographics were analyzed using the Chi-square test. Quiz scores were analyzed between groups using Kruskal–Wallis tests followed by Dunn’s post-tests for multiple comparisons. Values of p ≤ .05 were considered significant. On the short-term and retention quiz questions, students participating in SDL scored significantly higher (10 [5–10]) than those receiving in-person instructor demonstration (9 [3–10] p = .01 and 8 [2–10] p = .0004, respectively) or the online module instruction training (Group C) (6 [1–10] p < 0.0001 and 8 [4–10] p < .001, respectively). Based on quiz scores, veterinary students exhibited better ultrasound knobology and image quality recognition proficiency immediately and at 6-weeks following SDL when compared with other content delivery methods. Self-directed learning methods are recommended when teaching ultrasonography to veterinary students.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".