Socio-Economic and Demographic Determinants of Health Insurance Consumption
Bibliographic record
Abstract
This study analyzed factors affecting health insurance consumption in Akwa Ibom State. Primary data were collected from a total of 60 national Health Insurance Scheme patrons and non patron. Data were collected on consumer’s education, income, age; religion, sex, marital status, access to health insurance information, occupation and family size. The data were analyzed using descriptive statistics and regression analysis. The socio-economic and demographic profile of the people revealed that most of the sampled NHIS patrons and non patrons were literate, engaged in meaningful employment, mostly married with average income, and were still in their active ages, and demonstrated meaningful exposure to insurance health information, which enable them to be or not to be active participants of the scheme. From the regression analysis, it was evident that all the variables except religion influenced insurance consumption positively whereas religion affects health insurance consumption negatively. Based on this, we recommended among other things, a re-alignment of health insurance marketing strategies with consumers socio-economic and demographic characteristics, as a measure to boost patronage. Key words: Socio-Economic; Demographic; Health insurance; Consumption
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".