Oral Health Among Children and Youth With Special Health Care Needs
Bibliographic record
Abstract
OBJECTIVES We sought to estimate the prevalence of oral health problems and receipt of preventive oral health (POH) services among children and youth with special health care needs (CYSHCN) and investigate associations with child- and family-level characteristics. METHODS We used pooled data from the 2016–2018 National Survey of Children’s Health. The analytic sample was limited to children 1 to 17 years old, including 23 099 CYSHCN and 75 612 children without special health care needs (non-CYSHCN). Parent- and caregiver-reported measures of oral health problems were fair or poor teeth condition, decayed teeth and cavities, toothaches, and bleeding gums. POH services were preventive dental visits, cleanings, tooth brushing and oral health care instructions, fluoride, and sealants. Bivariate and multivariable logistic regression analyses were conducted. RESULTS A higher proportion of CYSHCN than non-CYSHCN received a preventive dental visit in the past year (84% vs 78%, P < .0001). Similar patterns were found for the specific preventive services examined. However, CYSHCN had higher rates of oral health problems compared with non-CYSHCN. For example, decayed teeth and cavities were reported in 16% of CYSHCN versus 11% in non-CYSHCN (P < .0001). In adjusted analyses, several factors were significantly associated with decreased prevalence of receipt of POH services among CYSHCN, including younger or older age, lower household education, non-English language, lack of health insurance, lack of a medical home, and worse condition of teeth. CONCLUSIONS CYSHCN have higher rates of POH service use yet worse oral health status than non-CYSHCN. Ensuring appropriate use of POH services among CYSHCN is critical to the reduction of oral health problems.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".