Dental Sleep Medicine Education Amongst Accredited Orthodontic Programmes in Thailand
Bibliographic record
Abstract
BACKGROUND: Dental sleep medicine education (DSME) should be emphasised in postgraduate orthodontic training; however, there appears to be no clear guideline for its implementation into the curriculum. OBJECTIVE: The aim was to investigate the current status of DSME as well as its feasibility and implementation in postgraduate orthodontic programmes. METHODS: A structured interview with predetermined response options was chosen as a data collection method to gather relevant information from representatives of all accredited postgraduate orthodontic programmes in Thailand. These interviews were conducted online via the Cisco Webex Meeting platform. A combination of data analysis techniques was employed to achieve a thorough comprehension of the research findings, including descriptive statistics, quantitative content analysis, thematic analysis, and alignment analysis. RESULTS: All participating programmes reported the inclusion of DSME in their curricula. A didactic approach was adopted by all programmes. However, only 2 out of 7 programmes offered clinical sessions for their students. Several challenges in implementing DSME within orthodontic programmes were identified, including a shortage of expertise and limited patient access. The participants also suggested that knowledge and resource sharing amongst institutions could serve as a potential solution to enhance the feasibility of DSME. CONCLUSIONS: This research highlighted the significant disparities and inadequacy of DSME within postgraduate orthodontic programmes in Thailand due to various challenges. Consequently, there is a compelling need to place greater emphasis on DSME and establish a national-level standardisation within orthodontic programmes. This effort is essential for enhancing the awareness and competency of orthodontists in the field of DSME.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".