Lessons from a Pilot Project in Cognitive Task Analysis: The Potential Role of Intermediates in Preclinical Teaching in Dental Education
Bibliographic record
Abstract
The purpose of this study was to explore the use of cognitive task analysis to inform the teaching of psychomotor skills and cognitive strategies in clinical tasks in dental education. Methods used were observing and videotaping an expert at one dental school thinking aloud while performing a specific preclinical task (in a simulated environment), interviewing the expert to probe deeper into his thinking processes, and applying the same procedures to analyze the performance of three second-year dental students who had recently learned the analyzed task and who represented a spectrum of their cohort's ability to undertake the procedure. The investigators sought to understand how experts (clinical educators) and intermediates (trained students) overlapped and differed at points in the procedure that represented the highest cognitive load, known as "critical incidents." Findings from this study and previous research identified possible limitations of current clinical teaching as a result of expert blind spots. These findings coupled with the growing evidence of the effectiveness of peer teaching suggest the potential role of intermediates in helping novices learn preclinical dentistry tasks.
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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 | 0.019 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".