Comparison Patients and Staffs Satisfaction in General Versus Special Wards of Hospitals of Jahrom
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Patient satisfaction is the most important indicator of high-quality health care and is used for the assessment and planning of health care. Also, Job satisfaction is an important factor on prediction and perception of organizational manner. The aim of this study is to identify and compare patient and staff satisfaction in general versus special wards. MATERIAL AND METHOD: In order to identify the various indicators of satisfaction and dissatisfaction, a descriptive study (cross sectional) was done to assess patients' satisfaction with in-patient care at Jahrom University of Medical Science hospitals. The sample size was 600 patients that selected by sequential random sampling technique and are close to their discharge from the hospital. Patients were asked to indicate the scale point which best reflected their level of satisfaction with the treatment or service. Also we assess the staff satisfaction (sample size was 408 staffs) in general ward using a researcher made questionnaire. It should be noted that the participants were anonymous and there was no obligation to participation. We tried to set a secure and comfortable environment for filling out the questionnaire. RESULTS: Among 600 patients, 239 (n=38.67%) were men and 368 (61.33%) were female. Number of nurses was 408, of which 135 (33.08%) were men and 273 (66.92%) female. There was a significant correlation between working experience and professional factors of personnel. The mean total patient satisfaction in general and special wards is (2.75±.35, 3.03±.53) respectively. Differences of patient satisfaction in domains such respect, care and confidence in general wards versus special ward were statistically significant, but there was no difference in expect time of patients in these wards. Differences Between the mean patient and staff satisfaction in the general wards versus special wards were statistically significant using independent t-tests (p=.018, p=.029). Spearman test showed a statistically significant correlation between patient and staff satisfaction (p=.044). CONCLUSION: For improving quality of medical services and effective functioning needs maximizing efforts to obtain full patient and staff 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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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 | 0.000 |
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".