Health System’s Responsiveness of Inpatients: Hospitals of Iran
Bibliographic record
Abstract
INTRODUCTION: Additional to improving health and ensuring equitable financing that are two predominant goals of health system, another important goal of health systems is responsiveness to people's non-medical expectations. In this study we try to assess the health system's responsiveness in academic and non-academic hospitals. METHODS: This is a cross sectional study done in summer 2014 in Mashhad-Iran, we surveyed a total number of 403 inpatients by multi-stage sampling. A questionnaire of responsiveness and a check list included demographic variables and characteristics of hospitalization were completed by trained interviewers. Scales from 0 to 10 was applied for each questionnaire at the end of assessment of questions. RESULT: 403 participants Took part in this survey from 10 hospitals (6 academic and 4 non-academic hospitals). 124(30.8%) were from non-academic and 279(69.2%) from academic hospitals 140(34.7%) of patients were male and 263(65.3%) were female. mean age of participants was 36.77±1.52 years. The mean total score of responsiveness was 7.12±1.31 in academic hospitals and 6.99±1.38 in non-academic hospitals, considered as good performance. There was no significant difference between total scores of these two groups (p=0.38). Health care responsiveness score was higher in private (8.35±0.95) than other kinds of hospitals and charity hospitals had the lowest score (5.98±0.51). CONCLUSION: Responsiveness of health care system at hospitals is an important parameter for measuring patients' perception of quality of health care. Although responsiveness rate of our hospitals are good but some components such as: choice health care providers, respect to autonomy of individuals, clear communication and confidentiality received lower responsiveness scores, therefore they require more attention and these domains can be the more significant choices that should be considered while designing improvement programs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".