Restructuring Saudi Board in Restorative Dentistry (SBRD) curriculum using CanMEDS competency
Bibliographic record
Abstract
OBJECTIVE: The purpose of this paper is to describe the process of adopting the Canadian Medical Education Directions for Specialists (CanMEDS) 2015 competency framework in a dental specialty program to reconstruct the Saudi Board in Restorative Dentistry (SBRD) curriculum and disseminate the lessons learned. Method and development process: The process of curriculum development was started with the selection of SBRD curriculum committee and review of CanMEDS framework. The Committee conducted needs assessment among the stakeholders and adopted CanMEDS 2015 competencies through a careful process. A modeled curriculum was developed after taking feedback, review of existing literature, and unique context of dentistry. Curriculum: Several unique features are incorporated. For example, milestones and continuum of learning are developed to enable residents develop competencies at different stages (transition to discipline, foundation of discipline, and core of discipline). Academic activities are restructured to encourage interactive, student-centered approaches, team work, intellectual curiosity, and scholarship. Learning outcomes are integrated throughout within several modules. Many formative assessment tools are adopted to promote learning and evaluate clinical skills. CONCLUSIONS: This is the first published example of adopting CanMEDS competency framework in a dental specialty program. The success of developing SBRD curriculum has encouraged other dental specialties toward adopting CanMEDS 2015 frameworks for their own curricula.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".