Evaluation of Traditional Instruction versus a Self-Learning Computer Module in Teaching Veterinary Students How to Pass a Nasogastric Tube in the Horse
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effectiveness of a self-learning computer module (SLCM) versus traditional instruction in teaching how to pass a nasogastric tube (NG) in the horse. DESIGN: A double-blind, monocentric study. SAMPLE POPULATION: 52 third-year students in the DVM program were randomly assigned to two groups: traditional instruction (N = 25) or SLCM instruction (N = 27). PROCEDURE: Traditional instruction consisted of an instructor and live demonstration; SCLM students were given a CD-ROM each. Both sessions lasted one hour. The students were then united in one session to practice passing the NG tube. Their performance was videotaped and evaluated by two evaluators. Students were then given a multiple-choice knowledge quiz. One week later, a second demonstration of the same procedure by the two methods was administered, and students were allowed to choose either method of instruction. A Likert-scale questionnaire about their comfort, their confidence, and the appropriateness of the teaching method was given after the second demonstration. Data were analyzed by non-parametric tests. A focus-group study was conducted to determine students' perception of each teaching method. Nine participants in the experiment volunteered for these focus-group sessions. The sessions were audiotaped and transcribed. RESULTS: Students in the SLCM group performed significantly better on the test of knowledge than traditionally instructed students. The questionnaire found significant perceived benefits to computer-based instruction, including a preference for the computer-based module, better learning, and greater preparedness. In hands-on skill, time to pass the NG tube on the successful attempt was significantly shorter in the SLCM group than in the traditionally instructed group. The data from focus-group sessions suggest that while participants expressed satisfaction with both modes of instruction, the SLCM group reported somewhat higher levels of confidence in their skills prior to performing the procedure. Whereas the traditional group reported a strong preference for continued live demonstrations of the procedure, the SLCM group stated that the computer-assisted module alone provided them with effective instruction. CONCLUSION: Computer-assisted learning is an acceptable and effective method of training students to pass an NG tube with potential welfare, proficiency, and knowledge advantages.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".