Comparison of dental licensure, specialization and continuing education in five countries
Bibliographic record
Abstract
Dental practice and education are becoming more globalized. Greater practitioner and patient mobility, the free flow of information, increasingly global standards of care and new legal and economic frameworks (such as European Union [EU] legislation) are forcing a review of dental licensure, specialization and continuing education systems. The objective of this study was to compare these systems in Canada, France, Germany, the UK and the US. Representatives from the five countries completed a 29-item questionnaire, and the information was collated and summarized qualitatively. Statutory bodies are responsible for licensing and re-licensing in all countries. In the two North American countries, this responsibility rests with individual states, and in Europe, with the countries themselves, mainly governed by the legal framework of the EU. In some countries, re-licensure requires completion of continuing education credits. Approaches to dental specialization tend to differ widely with regard to definition of specialities, course and duration of training, training facilities, and accreditation of training programmes. In most countries, continuing education is provided by a number of different entities, such as universities, dental associations, companies, institutes and private individuals. Accreditation and recognition of continuing education is primarily process-driven, not outcome-orientated. Working towards a global infrastructure for dental licensing, specialization and continuing education depends on a thorough understanding of the international commonalities and differences identified in this article.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".