Patient satisfaction and their determinants in outpatient department of a tertiary public hospital in Nepal: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Patient satisfaction is a vital metric for assessing healthcare quality and delivering patient-centered care. It can predict service utilization patterns by determining healthcare users' contentment with their providers. Consequently, evaluating patient satisfaction and its underlying factors is crucial to maintaining the quality of healthcare services. The present study aimed to assess patient satisfaction and its determinants in a tertiary care public hospital in Nepal. In this research, a cross-sectional design was employed to examine patient satisfaction within the Outpatient Department of Mental Hospital Lagankhel, Nepal. The study adopted a systematic random sampling approach for respondent selection, and stringent measures were implemented to uphold the validity and reliability of the collected data. To assess patient satisfaction comprehensively, the Patient Satisfaction Questionnaire-III (PSQ-III), developed by the RAND Corporation, was employed in conjunction with relevant sociodemographic variables. Utilizing mean scores and percentages, we calculated satisfaction levels across various dimensions. Additionally, a multinomial logistic regression analysis was conducted to investigate the relationships between patient satisfaction dimensions and sociodemographic characteristics. RESULTS: This study encompassed perspective of 206 participants, with 57.3% representing patient relatives and 51% being male, median age of 32 years (standard deviation: 12.53). Notably, patients reported higher levels of satisfaction, particularly within the interpersonal relationship dimension, while the technical quality domain received comparatively lower satisfaction ratings. Multinomial logistic regression analysis underscored the significance of sociodemographic factors in shaping patient satisfaction, with age (p = 0.008), type of residence (p = 0.001), occupation (p = 0.0019), income status (p = 0.014), time to reach the healthcare facility (p = 0.013), and insurance enrollment status (p = 0.017) all demonstrating significant associations. These findings illuminate the intricate qualities of patient satisfaction within our healthcare context, offering actionable insights for enhancement and guiding the trajectory of future research endeavors. CONCLUSIONS: Overall patient expressed satisfaction with service provided by tertiary care hospital, however continuous improvement remains essential. Conducting large-scale, nationwide studies across hospital tiers is vital. This data-driven approach empowers policymakers to allocate resources effectively, inform decision-making, and enact policies that exceed patient expectations, fostering a healthcare system of unparalleled excellence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".