Simulation-Based Comprehensive Cleft Care Workshops: A Reproducible Model for Sustainable Education
Bibliographic record
Abstract
Objective: Evaluate simulation-based comprehensive cleft care workshops as a reproducible model for education with sustained impact. Design: Cross-sectional survey-based evaluation. Setting: Simulation-based comprehensive cleft care workshop. Participants: Total of 180 participants. Interventions: Three-day simulation-based comprehensive cleft care workshop. Main Outcome Measures: Number of workshop participants stratified by specialty, satisfaction with the workshop, satisfaction with simulation-based workshops as educational tools, impact on cleft surgery procedural confidence, short-term impact on clinical practice, medium-term impact on clinical practice. Results: The workshop included 180 participants from 5 continents. The response rate was 54.5%, with participants reporting high satisfaction with all aspects of the workshop and with simulation-based workshops as educational tools. Participants reported a significant improvement in cleft lip (33.3 ± 5.7 vs 25.7 ± 7.6; P < .001) and palate (32.4 ± 7.1 vs 23.7 ± 6.6; P < .001) surgery procedural confidence following the simulation sessions. Participants also reported a positive short-term and medium-term impact on their clinical practices. Conclusion: Simulation-based comprehensive cleft care workshops are well received by participants, lead to improved cleft surgery procedural confidence, and have a sustained positive impact on participants’ clinical practices. Future efforts should focus on evaluating and quantifying this perceived positive impact, as well reproducing these efforts in other areas of need.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".