Using Cognitive Task Analysis to Develop a Protocol for Teaching Ultrasound Pregnancy Diagnosis in the Bitch to Undergraduate Veterinary Students
Bibliographic record
Abstract
Pregnancy diagnosis in the bitch is routinely performed using ultrasound and is therefore an important skill for veterinarians to have been exposed to during undergraduate training. Proficiency of this skill is difficult to achieve, due to limited exposure to suitable live patients, and animal welfare considerations limiting repeated performance on the same bitch. Models have been beneficial in allowing undergraduates to perform a range of ultrasound techniques without the use of live animals. Using clinical veterinarians and a model created at the University of Surrey, cognitive task analysis (CTA) was used to construct a list of instructional steps required to perform ultrasound pregnancy diagnosis. Experts were asked to evaluate the existing model then video recorded while demonstrating the skill on the model as if teaching a novice student. Anonymized and muted video footage along with transcribed audio files were used to create a draft teaching protocol. A group consensus for the final teaching protocol was developed following a semi-structured interview. The final teaching protocol had 23 steps to guide a novice to perform this skill, broken down into three stages: setup and preparation, pregnancy identification, and estimation of gestational age. Not all steps were both performed and verbalized by all of the experts, hence the need for a panel discussion to confirm a final teaching protocol. This study demonstrated that CTA is useful in compiling a comprehensive list of steps, for a teaching protocol, including those which may have been missed if demonstrated through a lone subject matter expert.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".