Measuring the Effectiveness of Small-Group and Web-Based Training Methods in Teaching Clinical Communication: A Case Comparison Study
Bibliographic record
Abstract
Current teaching approaches in human and veterinary medicine across North America, Europe, and Australia include lectures, group discussions, feedback, role-play, and web-based training. Increasing class sizes, changing learning preferences, and economic and logistical challenges are influencing the design and delivery of communication skills in veterinary undergraduate education. The study's objectives were to (1) assess the effectiveness of small-group and web-based methods for teaching communication skills and (2) identify which training method is more effective in helping students to develop communication skills. At the Ross University School of Veterinary Medicine (RUSVM), 96 students were randomly assigned to one of three groups (control, web, or small-group training) in a pre-intervention and post-intervention group design. An Objective Structured Clinical Examination (OSCE) was used to measure communication competence within and across the intervention and control groups. Reliability of the OSCEs was determined by generalizability theory to be 0.65 (pre-intervention OSCE) and 0.70 (post-intervention OSCE). Study results showed that (1) small-group training was the most effective teaching approach in enhancing communication skills and resulted in students scoring significantly higher on the post-intervention OSCE compared to the web-based and control groups, (2) web-based training resulted in significant though considerably smaller improvement in skills than small-group training, and (3) the control group demonstrated the lowest mean difference between the pre-intervention/post-intervention OSCE scores, reinforcing the need to teach communication skills. Furthermore, small-group training had a significant effect in improving skills derived from the initial phase of the consultation and skills related to giving information and planning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.033 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".