Comprehending the Number of Individuals with Disabilities and the Need for Oral Health Services
Bibliographic record
Abstract
INTRODUCTION: The use of mega-large numbers and percentages to describe the one billion people with disabilities in the world is beyond the comprehension of most people. We find it difficult to personalize such information and tend to skip over the data without considering the multitude of factors that impact on individuals with disabilities and their families. STUDY DESIGN: A review of World Health Organization, U.S. Census Bureau, and Canadian and U.S. dental school accreditation agency documents were used to establish the current information on disability numbers, proportions and dental education programs. RESULTS: More meaningful details from government agencies and the health professions and their educational institutions can provide data that could be used to demonstrate the increasing number of individuals with disabilities in a more meaningful manner; as well as preparing health professionals to provide the needed care. DISCUSSION: The use of survey data for specific countries by: age, types of disabilities, race/ethnicity, family and individual economics, employment and regional distribution provides a more personalized presentation which can be used to reach legislative bodies and health providers.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".