Competencies for dental public health specialists: A thematic analysis
Bibliographic record
Abstract
INTRODUCTION: Competency frameworks have been used to accurately guide the training and assessment of professionals. Dental Public Health professionals require a variety of skills beyond clinical aspects to meet ongoing social, economic, epidemiologic, technological, etc. developments. The purpose of this study was to develop a primary competency framework for dental public health (DPH) professionals by reviewing existing documents that can be modified by authorities based on their needs. MATERIALS AND METHODS: To identify DPH competencies, first a literature review of current postgraduate DPH competencies was conducted in PubMed, Scopus, Google Scholar, and Google from May to June 2021. All English language documents addressing DPH competencies were included and transferred to MAXQDA software. Next, DPH competency domains were extracted and defined, using Clarke and Braun's six-step qualitative thematic analysis method. RESULTS: In total, 206 English documents were retrieved. After exclusion of 201 documents due to being duplicate or not related in screening stages, five full-text English documents describing competencies of DPH specialists from the United Kingdom, the United States, Australia and New Zealand, Canada, and Ireland were reviewed. Thematic analysis led to the provision of a framework consisting of all mentioned competencies in the reviewed documents including nine domains in education, research, management, policy, communication, leadership, professionalism, oral health status, and oral health services. CONCLUSION: The proposed primary framework covers all competency domains and, as a comprehensive tool, can be used as a guide by local, national, and international authorities to develop their own frameworks for training and evaluating the DPH workforce.
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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 | 0.044 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 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".