Developing Common Competencies for Southeast Asian General Dental Practitioners
Bibliographic record
Abstract
Current policy in Southeast Asian dental education focuses on high-quality dental services from new dental graduates and the free movement of dental practitioners across the region. The Southeast Asian Nations (ASEAN) Dental Councils have proposed the "Common Major Competencies for ASEAN General Dental Practitioners" to harmonize undergraduate dental education. This article discusses how the ASEAN competencies were developed and established to assist the development of general dental practitioners with comparable knowledge, skills, and attitudes across ASEAN. The competencies were developed through four processes: a questionnaire about current national oral health problems, a two-round Delphi process that sought agreement on competencies, a panel discussion by representatives from ASEAN Dental Councils, and data verification by the representatives after the meeting. Key themes of the ASEAN competencies were compared with the competencies from the U.S., Canada, Europe, Australia, and Japan. A total of 33 competency statements, consistent with other regions, were agreed upon and approved. Factors influencing the ASEAN competencies and their implementation include oral health problems in ASEAN, new knowledge and technology in dentistry, limited institutional resources, underregulated dental schools, and uneven distribution of dental practitioners. The ASEAN competencies will serve as the foundation for further developments in ASEAN dental education including policy development, curriculum revision, quality assurance, and staff development. Collaboration amongst stakeholders is essential for successful harmonization of ASEAN dental 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 | 0.021 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".