Rural-urban disparities in patient satisfaction with oral health care
Bibliographic record
Abstract
Background: Identifying spatial variation in patient satisfaction is essential to improve quality of care.Thus, the objective of this study was to investigate rural-urban disparities in patient satisfaction and determine factors that could influence satisfaction with oral health care. Methods: Data from 1,788 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 patient characteristics, predisposing factors and enabling resources.Statistical analyses were comprised of descriptive statistics, as well as bivariate and linear regression models.Results: Individuals with higher income, dental insurance coverage, having a family dentist, having ease in finding a dentist and 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 scores 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).The multiple linear regression model showed that major determinants of patient satisfaction were being a native Canadian, married, having dental insurance coverage, having perceived good oral health, having a family dentist and having visited the dentist for regular checkups (p <0.005).Having difficulty finding a dentist negatively influenced patient satisfaction with oral heath care (p < 0.001).vii Conclusion: These findings suggest that Quebec rural-urban disparity exists in patient satisfaction with care and that determinants of health are predictors of patient satisfaction.Intensive and powerful knowledge dissemination activities will help to mobilize policy makers in implementing public health strategies to reduce this disparity.viii
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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 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".