Comprehending the Number of Individuals with Disabilities and the Need for Oral Health Services
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it