Service Quality and Patient Satisfaction in Low-Income Countries: Evidence from Uganda
Bibliographic record
Abstract
OBJECTIVE: The impact of service quality on in-patients’ satisfaction is important for global livelihood. The purpose of this study was to examine the impact of service quality on in-patients’ satisfaction. METHODS: Data was collected using semi-structured interviews from a Faith based hospital services. Multiple regression analysis was used to examine the impact of service quality on in-patients’ satisfaction. RESULTS: This study revealed service dissatisfaction was mainly influenced by tangibles (hospital infrastructure, cleanliness, and staff appearance) depicting the highest impact on patient satisfaction, followed by responsiveness (waiting time, billing, and discharge efficiency) with a weaker correlation, and empathy (doctor-patient communication and emotional support), having the lowest but still significant effect. The regression model confirmed three dimensions accounted for 53.8% of the variation in patient satisfaction. CONCLUSION: Critical areas for improvement, include modernizing hospital infrastructure, streamlining administrative processes, and enhancing patient-centred care. Automating hospital records, reducing waiting times, and integrating communication training for healthcare providers. The research aligns with the current studies in low- and middle-income countries, where structural improvements and service efficiency have a greater impact on patient satisfaction than interpersonal aspects alone. The study provides policy insights for hospital administrators, policymakers, and healthcare providers, emphasizing the need for a holistic approach to healthcare service delivery globally.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".