Sociodemographic Determinants of Patients’ Satisfaction with the Quality of Care in the General Hospitals in Ebonyi State, Nigeria
Bibliographic record
Abstract
INTRODUCTION: The purpose of the study was to assess sociodemographic determinants of patients' satisfaction with the quality of care in the General Hospitals in Ebonyi State. Four hypotheses were formulated for the study. Demographic characteristics of age, level of education, marital status, and income level on patients' satisfaction were ascertained. METHODS: A cross-sectional survey research design was used for the study. The population of the study comprised 1, 363, 633 (18 years and above) who attended general out-patient clinics in the General Hospitals using a sample of 400. Data were analyzed using mean ( ), t-test, and analysis of variance (ANOVA) were used to answer the hypothesis at 0.05 level of significance. The SPSS version 20 was employed for the analysis. RESULTS: Findings showed that patients who were 40–60 years ( = 2.96), had tertiary education ( = 2.97), earned income of N40, 000 – N59, 000 ( = 2.96) and were married ( = 3.09) were most satisfied. Besides, age, marital status, and income were not significantly associated (p>0.05) with patients’ satisfaction while the level of education was significant (p<0.05). CONCLUSION: The study revealed that older age, more educated, middle-class income earners, being married were more satisfied with the quality of care received. Efforts should be made by Health workers to ensure that all patients are satisfied irrespective of their demographic characteristics.
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 | 0.000 | 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) | 0.002 | 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".