The use of 'Precision Teaching' in enhancing medical students’ dermatological diagnostic skills
Bibliographic record
Abstract
<ns4:p>This article was migrated. The article was not marked as recommended. BackgroundEducators have been challenged to provide more effective dermatology teaching methods. Drawing from the discipline of Applied Behaviour Analysis, Precision Training (PT) (e.g. using flashcards during timed learning sessions) can promote fluency i.e. accuracy and speed in a particular skill. We aimed to determine the impact of PT on medical students' dermatology diagnostic skills.MethodsA between-groups controlled interventional study was conducted. Third year medical students were allocated to an intervention (PT + traditional teaching) or control (traditional teaching) group. For the PT group, we designed 50 dermatological image flashcards. Flashcard practice (using the Say All Fast Minute Each Day Shuffle method) took place 2-3 times/day and students' data on accuracy recorded over 5 days. Pre / post-training tests were carried out to determine the impact of PT on students' diagnostic skills.ResultsIn total, 70 students (intervention group) / 65 (control group). Analysis of covariance was used to calculate the change score (comparing pre- and post-test). A statistically significant improvement of 8.8% (95% CIs; 4.9-12.7, p<0.001) was detected in the intervention group.ConclusionsThe findings of this study demonstrated a positive effect of PT on medical students' dermatology diagnostic skills. This study signals new pedagogical opportunities for PT in undergraduate dermatology teaching.</ns4:p>
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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 | 0.003 | 0.653 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".