Public Reproductive Health Facilities: A Client-Satisfaction Survey
Bibliographic record
Abstract
INTRODUCTION: Understanding clients’ perspectives on quality improvement programs is essential to achieve the goals of health services. Determining client satisfaction could help decision makers to implement programs fit to their needs as perceived by service providers and clients. This study aimed to assess the level of satisfaction among women attending health centers regarding the services received in governmental health facilities in Shiraz, southern Iran. METHOD: This cross-sectional study was performed in 24 urban health centers. Using systematic random sampling method, 8 clinics were assigned to each group. Then questionnaires were distributed among 240 married women in 15-49 year-old age group who had referred to selected clinics for receiving some services. For data analysis, SPSS version 15 software and Chi-square statistical procedure were used to evaluate clients’ satisfaction. RESULTS: Data showed that 101 out of 240 respondents were completely satisfied with the personnel as well as the health center. Furthermore, satisfaction was found to be the highest among clients of those centers ranked as middle class socioeconomic status, while no significant difference was found between centers based on their socioeconomic status. CONCLUSION: The results of the present study would enable policy-makers to effectively improve the quality of health care, keeping a balance between providers’ and patients’ perspectives on the quality of health care.
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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 | 0.002 | 0.003 |
| 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) | 0.003 | 0.001 |
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".