Rural–urban disparities in patient satisfaction with oral health care: a provincial survey
Bibliographic record
Abstract
BACKGROUND: Identifying spatial variation in patient satisfaction is essential to improve the quality of care. Thus, the objective of this study was to investigate rural-urban disparities in patient satisfaction and to determine the factors that could influence satisfaction with oral health care. METHODS: Data from 1788 parents/caregivers of children who participated in the Quebec Ministry of Health clinical study were subject to secondary analysis. The Perneger model of patient satisfaction was used as the conceptual framework for the study. Satisfaction with oral health care was measured using the WHO-sponsored International Collaborative Study of Oral Health Outcomes (ICS-II). Explanatory variables included predisposing factors and enabling resources. Statistical analyses included descriptive statistics, as well as bivariate and linear regression models. RESULTS: Individuals with higher income, dental insurance coverage, having a family dentist, reporting ease in finding a dentist, and having access to a private dental clinic were more satisfied with oral health care (p < 0.001). There were statistically significant differences between rural and urban Quebec residents in their ratings of patient satisfaction on four items, including dental office location (p = 0.013), dental equipment (p = 0.016), cost of dental treatment (p < 0.001), and cleanliness of dental office (p = 0.004), with greater satisfaction for urban dwellers. The multiple linear regression model showed that major determinants of patient satisfaction were being born in Canada, income ≥ 40,000$ CAD, having a family dentist, and having visited the dentist in the last year for regular checkups. However, ethnicity, having difficulty finding a dentist, and being in need of dental treatment negatively influenced patient satisfaction with oral health care. CONCLUSIONS: These findings suggest that Quebec rural-urban disparity exists in patient satisfaction with care and that determinants of health influence this outcome. Intensive and powerful knowledge dissemination activities are needed to mobilize policymakers in implementing public health strategies to reduce this disparity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".