Trends in Patient Satisfaction at a Tunisian Gynecology-Obstetrics Service: A Comparative Analysis Between 2023 and 2024
Bibliographic record
Abstract
Background: Monitoring trends in patient satisfaction is essential for evaluating healthcare quality improvement initiatives and adapting services to meet evolving patient needs. This is particularly important in gynecology-obstetrics services, where care quality directly impacts women's health outcomes and experiences. Objective: To compare patient satisfaction levels between 2023 and 2024 in the gynecology-obstetrics service at Ibn Al Jazzar University Hospital in Kairouan, Tunisia, and evaluate the impact of implemented interventions. Methods: A comparative cross-sectional study was conducted using data from two periods: January-March 2023 (n=276) and January-March 2024 (n=284). The same validated questionnaire developed by the General Directorate of Public Health Structures (DGSSP) and the Observatory on the Performance of Organizations and Health Systems at the University of Montreal (OPOSSUM) was used in both periods. The questionnaire assessed overall satisfaction and seven specific domains: accessibility, continuity, comprehensiveness, technical quality, humanization, environment, and conditions of stay. Results: Overall patient satisfaction increased significantly from 37% in 2023 to 52% in 2024 (p<0.001). Improvements were observed across all seven domains, with the most substantial increases in conditions of stay (5.1% to 24.3%, p<0.001), comprehensiveness of care (16.3% to 38.7%, p<0.001), and humanization (37.0% to 56.3%, p<0.001). The implementation of the GynéSatis mobile application was associated with higher satisfaction rates among users compared to non-users (58.7% vs. 47.6%, p=0.02). Conclusion: The significant improvement in patient satisfaction between 2023 and 2024 suggests that targeted interventions, including digital solutions and facility enhancements, can effectively address patient concerns and improve healthcare experiences. Continued monitoring and adaptation of improvement strategies are recommended to sustain and further enhance patient satisfaction.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".