Exploring the association of self‐rated oral health with self‐rated general and mental health among older adults in a resource‐poor context: Insights for advancing Sustainable Development Goal 3
Bibliographic record
Abstract
INTRODUCTION: Older adults in Ghana have been disproportionately affected by oral health issues such as caries and periodontitis. This situation calls for comprehensive attention within health and healthcare policies, due to the established connections between oral health and other aspects of health and well-being in high-income countries, including physical and mental health. However, there is a significant gap in the literature when it comes to exploring the association of oral health with physical and mental health in resource-constrained settings like Ghana. METHODS: To address this void, we collected a cross-sectional sample comprising older adults aged 60 and above (n = 1073) and analyzed self-rated health measures to investigate the relationship between oral health and general and mental health in Ghana. RESULTS: The results of our logistic regression analysis revealed a significant association: older adults who reported poor oral health were more likely to rate their general (OR = 5.10; p < .001) and mental health (OR = 4.78, p < .001) as poor, compared to those with good oral health, even after accounting for demographic and socioeconomic variables. CONCLUSIONS: Based on these findings, we discuss the policy implications of our findings, especially in the context of advancing Sustainable Development Goal 3 in Ghana and other resource-constrained settings.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".