Bibliographic record
Abstract
Despite concerns regarding the oral health of rural Canadians, estimates of oral health-related quality of life do not exist in published provincial data. The objective of this population-based cross-sectional study was to estimate rural-urban differences in oral health-related quality of life (OHRQoL). Using Andersen's behavioral model for health services utilization as the conceptual framework, data from 1,788 parents/caregivers of schoolchildren from 8 regions of Quebec, Canada were collected through the employment of a two-stage sampling technique. Place of residency was defined according to Statistics Canada's Census Metropolitan Area and Census Agglomeration Influenced Zone classification. The outcome of interest was OHRQoL, measured using a validated Oral Health Impact Profile-14 (OHIP-14) questionnaire. The prevalence, extent and severity of negative oral health impacts were calculated after applying data heightening. Descriptive statistics, bivariate analyses and binary logistic regressions were performed using SPSS version 22. Our results showed that residents of rural areas had poorer oral health-related quality of life than those living in urban zones (P=0.02). Rural residents reported higher negative daily-life impacts in pain, psychological discomfort and social disability OHIP-14 domains (P<0.05). Additionally, there was a statistically significant difference between rural and urban populations in the extent of negative oral health impacts (P=0.03). However, the difference in severity of poor oral health was not statistically significant for the two population groups. Logistic regression indicated that factors such as the place of residency (OR=1.6; 95%CI=1.1-2.5; P=0.022), perceived oral health (OR=9.4; 95%CI= 5.7-15.5; P<0.001), dental treatment needs factors (perceived need for dental treatment, pain, dental care seeking) (OR=8.7; 95%CI=4.8-15.6; P<0.001) and education (OR=2.7; 95%CI=1.8-3.9; P<0.001) significantly affect OHRQoL. Based on these findings, we conclude that there is a rural-urban difference in OHRQoL in Quebec, Canada. Policies focusing on oral health promotion, educational interventions and tailored need-based programs are necessary to improve the OHRQoL of vulnerable populations.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".