The global distribution of special needs dentistry across dental school curricula
Bibliographic record
Abstract
INTRODUCTION: Special needs dentistry (SND) is an emerging dental specialty, with ongoing developments in education and clinical practice focused towards the tailored management of individuals with special needs (SN). Patients with SN have a higher prevalence of oral diseases and unmet dental needs compared to the general population. Although inadequate training and experience in managing patients with SN has been highlighted as a significant barrier to accessing care, there is limited data about the extent of SND teaching at the entry-to-practice or higher levels. METHODS: This work is the first to map SND curricula globally, across 180 countries and 1265 dental schools. RESULTS: Although 74.62% of dental schools were found in developing economies, the distribution of programs that reported SND in their courses was highly skewed towards developed countries. In terms of advanced degrees, beyond basic entry-to-practice training, the USA delivered 60% of the SND programs, followed by Canada (15.56%), UK (13.33%), and Australia (8.89%). The term SND appeared in 33.95% of entry-to-practice level program curricula and was less commonly used in transitioning economies. Only 112 SND-specialized practitioners enter the workforce globally each year from developed economies, and all but three advanced degrees are found in G7 countries. CONCLUSION: By exploring the impact of economic status on its distribution, this paper highlighted the lack of SND representation in dental curricula, especially amongst programs in transitioning or developing economies. Education of both general dentists and specialists is critical as a collaborative effort is needed to manage the growing population of patients with SN.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".