Patient Satisfaction in a Gynecology- Obstetrics Service at Ibn Al Jazzar University Hospital in Tunisia: A Cross- Sectional Study (2023)
Bibliographic record
Abstract
Background: Patient satisfaction is a crucial indicator of healthcare quality, particularly in gynecology-obstetrics services where care involves intimate and sensitive aspects of women's health. Understanding patient perspectives is essential for improving service delivery and healthcare outcomes. Objective: To evaluate patient satisfaction levels in the gynecology-obstetrics service at Ibn Al Jazzar University Hospital in Kairouan, Tunisia. Methods: A cross-sectional study was conducted from January to March 2023 among 276 patients hospitalized in the gynecology-obstetrics service. Data was collected using a validated questionnaire developed by the General Directorate of Public Health Structures (DGSSP) in collaboration with the Observatory on the Performance of Organizations and Health Systems at the University of Montreal (OPOSSUM). The questionnaire assessed overall satisfaction and seven specific domains: accessibility, continuity, comprehensiveness, technical quality, humanization, environment, and conditions of stay. Satisfaction was measured using a 6-point Likert scale (0-5). Results: The overall satisfaction rate was 37%. Domain-specific satisfaction rates varied considerably: accessibility of care (65.6%), continuity of care (41.7%), comprehensiveness of care (16.3%), technical quality of care (52.9%), humanization (37.0%), environment (41.3%), and conditions of stay (5.1%). Physician competence (80.8%) and staff cleanliness and attire (83.7%) received the highest satisfaction ratings, while visiting areas (0%) and visiting hours (5.1%) received the lowest. Conclusion: While overall patient satisfaction in the gynecology-obstetrics service was moderate, significant variations existed across different domains. The findings highlight areas of strength and opportunities for improvement. Implementing targeted interventions, including digital solutions like the GynéSatis mobile application, could enhance patient experience and healthcare quality in this setting.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".