Competency model for dentists in China: Results of a Delphi study
Bibliographic record
Abstract
OBJECTIVE: With the increasing awareness of the importance of oral health, patients have an increasing need for integrated care from dentists. In China, the dentistry examination consists of two parts: a practical skills examination and a comprehensive medical examination; to date, no assessment methods that are based on specialized dentistry competencies, unlike the United States, Canada, and other countries, have been established. Therefore, the purpose of this study was to construct a competency model for dentists in China in order to guide the development, admission, training and assessment of dentists. METHODS: Using a literature review, focus group interviews and in-depth personal interviews, a dentist competency index was developed with an expert consultation questionnaire. A panel of 20 specialist experts was chosen from ten national medical universities to carry out two rounds of Delphi expert analysis, using the boundary value method to filter the indicators and the Analytic Hierarchy Process to calculate the weights of the primary indicators. RESULTS: Two rounds of Delphi results showed that the expert authority, enthusiasm, and coordination coefficients were high. Constructs of the competency model that included seven primary indicators and 62 secondary indicators determined the weight of each index. The seven primary indicators included the following: clinical skills and medical services, disease prevention and health promotion, interpersonal communication skills, core values and professionalism, medical knowledge and lifelong learning ability, teamwork ability and scientific research ability. CONCLUSION: In conclusion, the use of the Delphi method to construct an initial model of Chinese physician competency is scientific and feasible. The initial competency model conforms to the characteristics and quality requirements of dentists in China and has a strong scientific basis. The dentist competency model should be used in the National Dental Licensing Examination in China.
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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 | 0.027 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".