Intersection of the iADH undergraduate curriculum in special care dentistry and the association of Canadian faculties of dentistry competencies framework
Bibliographic record
Abstract
AIMS: To map the International Association of Disability and Oral Health (iADH) curriculum to the Association of Canadian Faculties of Dentistry (ACFD) competencies framework to develop a strategy for teaching special care dentistry (SCD) using the International Classification of Functioning, Disability, and Health (ICF). To review the literature to identify educational methodologies that support teaching SCD competencies. METHODS: The 20 subdomains of the ACFD competencies framework were mapped to the 18 subdomains of the iADH competency matrix. A literature review of methods, techniques, or innovations used to teach SCD was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines and the Sample, Phenomenon of Interest, Design, Evaluation, and Research Type (SPIDER) tool. RESULTS: The iADH curriculum was mapped to the ACFD competencies in the areas of patient care, professionalism, communication and collaboration, practice information management, and health promotion. A total of 176 articles from PubMed and 10 resources from MedEdPortal were identified in the literature search. Eleven articles met the inclusion and exclusion criteria. The overall quantity and quality of studies was low. Experiential learning in either a dental school or hospital-based program seemed to improve knowledge of SCD and to incite greater willingness to treat patients requiring SCD. CONCLUSIONS: Case-based learning, computer-based modules, standardized patients, and clinical practice are educational strategies for teaching SCD competencies. The integration of SCD into the undergraduate dental curriculum seems feasible, as most required competencies are transferable to all dental disciplines. Furthermore, the ICF provides a functional model that is a patient-centered approach and is applicable to dentistry beyond SCD.
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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 | 0.017 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".