The Relationship between Periodontitis and Oral Health Literacy among the Older People in Thailand
Bibliographic record
Abstract
OBJECTIVE: The purpose of this study was to evaluate the relationship between periodontitis and oral health literacy among the older population in Thailand. MATERIAL & METHODS: This cross-sectional study was performed between July 1 and September 30, 2020, in Panare district, Pattani province, Thailand. The inclusion criteria were older individuals more than 60 years of age who had at least six remaining teeth. Information on sociodemographic characteristics and oral health-related behaviors were collected using a self-reported questionnaire. Oral health literacy was categorized using the Thai version of the Health Literacy in Dentistry scale (HeLD‐Th). A trained examiner performed clinical periodontal examinations. The data were analyzed using the Mann-Whitney U test, Fisher's exact test, and binary logistic regression analysis. RESULTS: A total of 216 independently living older adults participated and completed the study protocol. The initial analyses indicated significant associations between severe periodontitis and low oral health literacy (p = 0.029) and insufficient toothbrushing duration (p < 0.001). However, in multivariate analysis, only toothbrushing duration showed significant association (p = 0.003). CONCLUSIONS: Oral health literacy interventions and oral hygiene practices for improving periodontal health status among the Thai older adults are necessary.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".