Factors Determining Inpatient Satisfaction with Hospital Care in Bangladesh
Bibliographic record
Abstract
The objective of this study is to identify factors associated with satisfaction among inpatients receiving medical and surgical care for urinary, cardiovascular, respiratory, and ophthalmology diseases at Dhaka Government Medical College Hospital, Bangladesh. The data of this study is collected from 190 inpatients by using a patient judgments questionnaire covering 10 dimensions of satisfaction (appointment waiting time for doctor after admission, doctor’s treatment and behavior, behavior and services of nurses, boys and ayas (-care givers-), toilet and bath room condition, quality of food, number of days in the hospital, cost for treatment, and gift/tips culture in the hospital). Patient overall level of satisfaction is treated as dependent variable, while dimensions of satisfaction are each treated as independent variables. Additionally, inpatients’ socio-economic characteristics such as education, occupation and monthly family income are used as independent variables. OLS regression models are used to identify key factors connected with inpatients satisfaction. The level of significance for variables retain in the regression models is set at 0.05. The final regression model is significant with F-value of 73.673 (p<0.001) and can explain 80.8% of the variation in the dependent variable as it is indicated by the R-Square. The nested model F-test suggests that inpatients’ monthly family income and levels of education have significant effect on the dependent variable.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".