Wings of Knowledge: Navigating Learner Confidence and Cognitive Load in Avian Radiography with a Low Fidelity Model
Bibliographic record
Abstract
In veterinary first opinion practice, radiography is an important diagnostic tool for avian patients. Teaching of such diagnostic skills to learners is usually conducted using teaching models in clinical skills laboratories. The aim of this work is to evaluate the impact of using a teaching model for avian radiography positioning by measuring the learner's cognitive load, confidence, satisfaction, and assessing learning by Objective Structured Clinical Examination (OSCE) assessment. An avian radiography positioning model was created and evaluated with pre- and post-Likert questions on confidence, a pre and post 9-point cognitive load scale, an OSCE assessment (max score = 20), and post Likert questions on satisfaction. Thirty-two undergraduate veterinary medicine and veterinary nursing students participated in the study. The results showed the cognitive load of participants was high and did not change with the use of a physical model ( p = .882). Participants exhibited increased confidence in avian radiography positioning (pre: M = 2, post: M = 4, p < .001) and expressed high overall satisfaction with the model ([Formula: see text] = 4.6, no negative or neutral Likert responses). The OSCE results demonstrated a higher pass rate mean (82%) for the positioning tasks compared to the collimation and centering tasks (53%). Overall, the model was well received by learners with increased confidence and a satisfactory learning experience in a clinical skill for exotic species. These findings suggest the avian radiography positioning model is an effective model to train students to position avian patients for radiography.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".