Impact of mood disorders on oral health‐care utilization among middle‐aged and older adults
Bibliographic record
Abstract
BACKGROUND: Good oral health improves quality of life and is an integral part of active aging. Similar to some other systemic diseases, mood disorders are more prevalent in middle to older ages and have an associated risk of developing poor oral health. Consequently, people with mood disorders need to have regular dental care. There is scarce evidence in Canada linking mood disorders to the use of professional oral care services. The purpose of this study was to investigate the association between mood disorders and utilization of oral health-care services in a population-based sample of middle aged and older adults in Canada. METHODS: Data were extracted from Canadian Community Health Survey - Healthy Aging, 2008. Multinomial logistic regression was used to investigate the association between mood disorders and oral care utilization, adjusted for the confounders. RESULTS: Among 30,354 respondents included in our sample, 2162 (6.9%) reported having mood disorders. After adjusting for age, sex, education, marital status, and dental insurance status, the respondents who had mood disorders had a significant increased odds of not visiting a dental professional in the past year (OR:1.21, 95% CI: 1.08-1.35). The association of never visiting a dental professional and mood disorders was even stronger (OR: 1.49, 95% CI: 0.91-2.46). CONCLUSION: Mood disorders were found to have a strong association with underutilization of oral care services among aging adults of Canada. Given the associated poor oral health risks for elderly with mood disorders, oral health planners should strengthen the implementation of surveillance programs directed toward better oral health for this disadvantaged subpopulation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".