Factors associated with patient satisfaction in a private health care setting in India: A cross-sectional analysis
Bibliographic record
Abstract
The present study was conducted to assess patient satisfaction and factors associated with it in a tertiary care hospital in India; and to evaluate the delay in discharge process and its association with satisfaction. It is a cross-sectional analysis of secondary data abstracted from patient satisfaction forms of 1,054 individuals. We analysed factors associated with rating of hospital services and overall hospital experience. We also evaluated the delay in discharge process and its association with overall satisfaction of these patients. We used regression models to assess factor associated with satisfaction scores and “good hospital experience”. About 91% of individuals reported that their experience in the hospital was good. The mean satisfaction scores were significantly lower in patients with delays in discharge due to insurance problems (-0.14, 95% CI: -0.27, -0.02). An increase in one unit in doctor’s score was significantly associated with “good rating” of hospital services (OR: 1.37, 95% CI: 1.19, 1.58). Similarly, one unit increase in the housekeeping score (OR: 1.34, 95% CI: 1.18, 1.52) and billing score (OR: 1.83, 95% CI: 1.56, 2.16) were significantly associated with an overall “good” rating. Thus, problems faced by patients and relatives during completion of billing procedures are important factors that determine overall satisfaction with health care settings. Improving the interpersonal and communication skills of doctors will be an important intervention for better hospital experience.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".