An effective and novel method for teaching applied facial anatomy and related procedural skills to esthetic physicians
Bibliographic record
Abstract
BACKGROUND: An understanding of facial anatomy is crucial for the safe practice of nonsurgical facial esthetic procedures. Contextual learning, aided with instructional design, enhances the trainees' overall learning experience and retention, and makes a positive impact on the performance of procedural skills. The present study aimed to develop a teaching approach based on Bloom's taxonomy involving cognitive, affective, and psychomotor learning domains. MATERIALS AND METHODS: The practicability of Assess & Aware, Demonstrate, Decode, Act & Accomplish, Perform, Teach & Test (ADDAPT), a new approach to teaching applied facial anatomy and procedural skills to esthetic physicians in a large group setting, was evaluated in this study. Study participants were from two cohorts (n=124) who underwent 2 days of applied anatomy training in Singapore. Pre- and post-course multiple choice questions and objective structured practical examination were conducted to measure the effectiveness and applicability of the teaching model. Expert raters, table demonstrators, and participants rated the steps involved in the ADDAPT model on an 11-point Likert scale. RESULTS: <0.001). Inter-rater agreement, expressed as the intraclass correlation coefficient, was 0.91 (95% CI: 0.62-0.98) for expert raters and 0.90 (95% CI: 0.78-0.97) for table demonstrators, which reflects the real strength of sound educational practice. The trainees well accepted the model and found the sessions intellectually stimulating. Trainees' feedback stated that the learning experience was enhanced by the repeated observation and constructive feedback provided by the tutors. CONCLUSION: The ADDAPT model is practical to instruct a large group of trainees in clinical anatomy and procedural skill training. This approach to instructional design may be feasible and transferable to other areas of psychomotor skill training in medical education.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".