Comparison of health-seeking behaviour of Malaysian adults in urban and rural areas pertaining to oral health issues: findings from the National Health and Morbidity Survey 2019
Bibliographic record
Abstract
Abstract Background: The development and implementation of appropriate strategies to enhance oral health in the community can be aided by an understanding of oral health-seeking behaviour among urban and rural populations. The purpose of this study was to identify the factors associated with oral health-seeking behaviour of the Malaysians in urban and rural locations who self-reported dental problems. Methods: The National Health and Morbidity Survey 2019, a cross-sectional nationwide household survey that focused on non-institutionalised Malaysians, provided the data for this study on adults in Malaysia who were 18 years of age and older. Data was collected using a multilingual (Malay and English), structured, and validated questionnaire via face-to-face interviews from July to October 2019. Complex sample descriptive statistics were used to describe the characteristics and oral health-seeking behaviour of the respondents. The relationship between predictors (predisposing, enabling, and health needs factor) and oral health-seeking behaviours were investigated using multivariable logistic regression analysis (sought healthcare and self-medication). Results: The analysis comprised a total of 10,134 respondents, or about 18.2 million Malaysian adults ages 18 and above. The overall prevalence of Malaysian adults who self-reported dental problems was low (5.5%) and was slightly higher in the rural than urban population. Almost half sought treatment from healthcare practitioners (HCPs), and almost a quarter self-medicated. Ethnicity was associated with seeking healthcare and self-medication among urban dwellers. Among the rural population, income level was associated with seeking healthcare while education level was associated with self-medication. Conclusion: Disparities in oral health-seeking behaviours exist between Malaysians living in urban and rural areas. Future policies should adopt focused strategies that concentrate on oral healthcare accessibility and health literacy of the vulnerable and rural populations to achieve the best oral healthcare for this population group.
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 | 0.001 |
| 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 | 0.000 | 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".