Rural‐urban disparity in oral health‐related quality of life
Bibliographic record
Abstract
OBJECTIVES: The objective of this population-based cross-sectional study was to estimate rural-urban disparity in the oral health-related quality of life (OHRQoL) of the Quebec adult population. METHODS: A 2-stage sampling design was used to collect data from the 1788 parents/caregivers of schoolchildren living in the 8 regions of the province of Quebec in Canada. Andersen's behavioural model for health services utilization was used as a conceptual framework. Place of residency was defined according to the Statistics Canada Census Metropolitan Area and Census Agglomeration Influenced Zone classification. The outcome of interest was OHRQoL measured using the Oral Health Impact Profile (OHIP)-14 validated questionnaire. Data weighting was applied, and the prevalence, extent and severity of negative oral health impacts were calculated. Statistical analyses included descriptive statistics, bivariate analyses and binary logistic regression. RESULTS: The prevalence of poor oral health-related quality life (OHRQoL) was statistically higher in rural areas than in urban zones (P = .02). Rural residents reported a significantly higher prevalence of negative daily-life impacts in pain, psychological discomfort and social disability OHIP domains (P < .05). Additionally, the rural population showed a greater number of negative oral health impacts (P = .03). There was no significant rural-urban difference in the severity of poor oral health. Logistic regression indicated that the prevalence of poor OHRQoL was significantly related to place of residency (OR = 1.6; 95% CI = 1.1-2.5; P = .022), perceived oral health (OR = 9.4; 95% CI = 5.7-15.5; P < .001), dental treatment needs factors (perceived need for dental treatment, pain, dental care seeking) (OR = 8.7; 95% CI = 4.8-15.6; P < .001) and education (OR = 2.7; 95% CI = 1.8-3.9; P < .001). CONCLUSION: The results of this study suggest a potential difference in OHRQoL of Quebec rural and urban populations, and a need to develop strategies to promote oral health outcomes, specifically for rural residents. Further studies are needed to confirm these results.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 3 models reading the full record.
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".