Scaling-up universal financial protection: experience from a tertiary health facility of the impact of perception on the willingness to enrol in health insurance schemes in Delta State, Nigeria
Bibliographic record
Abstract
Objective: To assess the evidence of how the perception of health insurance impacts on the willingness to enrol and utilize health insurance among clienteles using tertiary health services. Method: This was a descriptive cross-sectional study. The instrument was a pre-tested, semi-structured self administered questionnaire. Descriptive statistics as well as chi-square test and regression analysis were done to show statistically significant associations. Results: The findings reveal that majority of the respondents, that is 109 (46.4%) were of the opinion that health insurance is a viable programme, however they had their reservations, which were those of uncertainty, amongst others. Nevertheless, the perception by most of the respondents showed that they need more information based on their poor experiences of health insurance, and this strengthens their quest to enrol in any such scheme. A sufficiently reliable association between the feeling that they need more information on health insurance and the willingness to enrol in a health insurance scheme (χ2 = 11.690, df = 1, p-value = .001) was shown. Conclusion: The findings from this study has brought to the fore that perception of clients using health services impacts on their desire and willingness to participate in health insurance schemes. However, there are concerns that necessitate wide spread advocacy for health insurance.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".