Review of overseas dental regulatory authorities for a discussion on self-regulation of the dentist : Focused on International Society of Dental Regulators, the U.K., Ontario in Canada, California in the U.S. and Japan
Bibliographic record
Abstract
Recently, there has been an increasing interest in the regulation of medical & dental profession in South Korea due to various medical scandals & exacerbated commercialism. Consequently, the voice asking for strengthening the license management of medical & dental profession is rising. However, there is an absolutely lacking discussion on self-regulation of the Korean dentist community. This study investigated International Society of Dental Regulators and dental regulatory authorities in the U.K., Ontario in Canada, California in the U.S. and Australia. In addition, this study examined what situations Japan was in, which was similar to Korea in terms of systems. In the U.K., the U.S., Canada and Australia, there are independent dental regulatory authorities, which place emphasis on lay personnel participation. In addition, the organizations prepared very specific and detailed ethics, standards, and punishment guidelines to be followed by professionals. And, various efforts are being made to secure transparency and trust. As a result of this study, self-regulation in Korea seems to require an open approach that embraces civil society, and it is considered that dentist should lead social discussion more positively.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".