The Associations between Oral Health Literacy and Oral Health-Related Behaviours among Community-Dwelling Older People in Thailand
Bibliographic record
Abstract
OBJECTIVE: This study aimed to investigate the associations between oral health literacy and oral health behaviours among community-dwelling older adults in Thailand. MATERIAL & METHODS: This community-based cross-sectional study was conducted at the District Health Promoting Hospitals (DHPH), Panarae district, Pattani province, Thailand, between 1-30 June 2020. A total of 271 community-dwelling older adults participated in the study. The inclusion criteria were independent living elderly who were 60 years or over and had at least one remaining tooth. Those who had a communication problem, severe chronic diseases, or disabilities were excluded. Data were collected by questionnaire interviewing. Binary logistic regression was analyzed. RESULTS: In the final model of regression analyses, older age (OR = 1.810, p = 0.035), limited education levels (OR = 2.113, p = 0.027), and participants who had the frequency of tooth brushing less than two times per day (OR = 1.905, p = 0.047) were statistically significant predictors of lower oral health literacy levels. CONCLUSIONS: The findings confirmed the strong associations between OHL and age, education levels, including the frequency of toothbrushing in the participants. This evidence indicates that an appropriate education program about oral health promotion probably induce adequate oral health literacy among the older population.
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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 | 0.002 |
| 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) | 0.002 | 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".