Patient Satisfaction with Primary Health Care – A Comparison between the Insured and Non-Insured under the National Health Insurance Policy in Ghana
Bibliographic record
Abstract
Ghana has initiated various health sector reforms over the past decades aimed at strengthening institutions, improving the overall health system and increasing access to healthcare services by all groups of people. The National Health Insurance Scheme (NHIS) instituted in 2005, is an innovative system aimed at making health care more accessible to people who need it. Currently, there is a growing amount of concern about the capacity of the NHIS to make quality health care accessible to its clients. A number of studies have concentrated on the effect of health insurance status on demand for health services, but have been quiet on supply side issues. The main aim of this study is to examine the overall satisfaction with health care among the insured and uninsured under the NHIS. The second aim is to explore the relations between overall satisfaction and socio-demographic characteristics, health insurance and the various dimensions of quality of care. This study employs logistic regression using household survey data in three districts in Ghana covering the 3 ecological zones (coastal, forest and savannah). It identifies the service quality factors that are important to patients' satisfaction and examines their links to their health insurance status. The results indicate that a higher proportion of insured patients are satisfied with the overall quality of care compared to the uninsured. The key predictors of overall satisfaction are waiting time, friendliness of staff and satisfaction of the consultation process. These results highlight the importance of interpersonal care in health care facilities. Feedback from patients' perception of health services and satisfaction surveys improve the quality of care provided and therefore effort must be made to include these findings in future health policies.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".