Association of Canadian Faculties of Dentistry Educational Framework for the Development of Competency in Dental Programs
Bibliographic record
Abstract
The Association of Canadian Faculties of Dentistry (ACFD) recently developed a proposal that reflects its evolving understanding of competency-based dental education. The ACFD proposal was developed into an Educational Framework for the Development of Competency in Dental Programs and has been adopted by all ten Canadian dental schools as the basis for their ongoing curriculum development and assessment. This framework identifies five global competencies that provide a big picture of the complex skills, knowledge, and behaviors that dental graduates must demonstrate. Detail for clarification and illustration is provided by more comprehensive "components" of each area that elaborate on the global statement and by a new dimension that assists with assessment: "indicators" of the specific knowledge, skills, and behaviors that can be measured as steps towards developing competence. In the information supporting understanding and assessment of the five key areas are both the existing national competency statements to facilitate the use of the framework by other stakeholders and a parallel set of knowledge, skills, and abilities statements developed by the National Dental Examining Board of Canada (NDEB) as the starting point for updating its examination blueprints. This article outlines the development, structure, and contents of the ACFD Educational Framework in the hope that it can serve as the foundation for a new Canadian national competencies document serving all national stakeholders.
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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 | 0.030 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.005 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".