Determinants of Dental and Oral Health Care Service: A Meta-Analysis
Bibliographic record
Abstract
Background: The limited utilization of dental and oral health services leads to poor dental and oral health status of both individuals and community. Regular visits to dentists can improve oral health status through early detection of dental and oral diseases. The study aims to systematically examine the factors that influence the utilization of dental and oral health services. Subject and Method: It was a systematic review and meta-analysis study using PRISMA and PICO diagrams. P= general population. I = women, higher education, high income, poor self-perception, and having health insurance. C= male, low education, low income, good self-perception, and no health insurance. O= utilization of dental and oral health services. Data collection was conducted using the PubMed and ScienceDirect databases. The inclusion criteria used were full, English, cross-sectional design articles in 2012-2023. The keywords used are "(Determinant OR Factor associated)" AND "Dental healthcare utilization". Data analysis was performed using the RevMan 5.3 application. Result: There were14 primary articles as meta-analysis sources from Saudi Arabia, Indonesia, Iran, Korea, Thailand, Bosnia and Herzegovina, Sweden, the United States, Canada, and Brazil. Female (aOR= 1.13; CI 95%= 1.02-1.25; p= 0.020), higher education (aOR= 1.90; CI 95%= 1.40- 2.56; p<0.001), high income (aOR= 1.91; CI 95%= 1.55-2.35; p<0.001), and having health insurance (aOR= 1.68; CI 95%= 1.30-2.19; P<0.001) increased the utilization of dental and oral health services. Self-perception did not affect the utilization of dental and oral health services (aOR= 1.04; CI 95%= 0.81-1.33; p= 0.76). Conclusion: Female gender, education level, income level, and ownership of health insurance influence the utilization of dental and oral health services.
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.065 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".