Comparing the Efficacy of a New Clinical Skills Model with a Traditional Method to Teach Tube Feeding of an Avian Patient
Bibliographic record
Abstract
Interactive clinical skills models have been demonstrated to be useful for teaching medical and veterinary clinical skills, yet to date, very few exist for teaching skills relevant to zoological companion animals and wildlife species including birds. This two-part study aimed to create, develop, and validate a model. Interviews and a survey were conducted using veterinary and wildlife professionals to select an avian clinical skill that is challenging and performed frequently. Tube/gavage feeding, or “crop tubing” satisfied both criteria; on average it was performed 71 times a year by surveyed respondents and was rated 3.4/9 for difficulty of teaching and 3.5/9 for difficulty of learning. Therefore, a new model of a bird, made from a soft toy, silicone, and 3D printed parts, was designed to train students to perform this technique. Forty-two participants were recruited and divided into two groups; one used the model the other watched an instructional video on crop tubing. The students completed a self-evaluated confidence questionnaire, before and after, using either resource. They then performed the technique on a dead bird and their proficiency at 10 different actions that comprised the technique was evaluated by two assessors. The model group performed significantly better than the video group on all evaluated actions ( U ≤ 143.5, p ≤ .0031), and reported significantly higher confidence ( U = 129.5, p = 0.018). In conclusion, the newly developed model in combination with an instruction booklet offers an effective and inexpensive alternative way to teach crop tubing in a teaching environment, without compromising animal welfare.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".