Building Clinical Skills Teaching Materials: A Study of Cognitive Task Analysis Versus Free Recall Instruction to Teach Canine Indirect Ophthalmoscopy
Bibliographic record
Abstract
Ophthalmoscopy is a core component of a complete ophthalmic examination. Due to its complex technical aspects and patients becoming uncooperative if the procedure is prolonged, it can be a difficult skill for a novice to learn and develop proficiency in. Skills instruction is typically provided by subject matter experts (SMEs) through free recall without an agreed-upon instructional framework. This can lead to unintentional omission of essential steps and knowledge required to perform skills correctly. Cognitive task analysis (CTA) allows for construction of standardized instructional protocols that encompass the knowledge and skills experts apply when performing tasks. The objectives of this study were to (a) develop a CTA-based teaching protocol for canine indirect ophthalmoscopy and (b) compare the steps verbalized or demonstrated by SMEs during free recall instruction versus those ultimately identified by CTA. Four SMEs participated in free recall instructional sessions and interviews used for the development of a CTA-based teaching protocol for novice learners. The CTA-based protocol identified 66 steps and sub-steps considered essential for successfully performing canine indirect ophthalmoscopy. During instructional sessions, SMEs on average failed to verbalize 57.1% of clinical knowledge steps, did not verbalize or demonstrate 68.3% and 9.5% of action steps, and did not verbalize or demonstrate 73.2% and 40.4% of decision steps, respectively. This study demonstrates that SMEs teaching indirect ophthalmoscopy by free recall may unintentionally omit important steps, suggesting that compared with free recall, CTA may generate more comprehensive and thus potentially more effective instructional materials for teaching technical skills in veterinary medicine.
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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 | 0.014 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".