Factors Associated to the Enrollment in Health Insurance: An Experience from Selected Districts of Nepal
Bibliographic record
Abstract
The enrollment in Health Insurance (HI) is considered as a sustainable way of financing for health and preparedness for catastrophic health care cost during receiving health services. Various socio-demographic factors are still unanswered regarding their influence. A study aiming to assess the factors associated with the enrollment of HI was conducted in 2018 in two districts of Nepal namely Kailali and Baglung. The study was cross-sectional covering 810 (405 enrolled and 405 not-enrolled) randomly selected households (HH). Socio-demographic variables were considered as independent variables and enrollment in HI as dependent variable. An interview schedule was used as a tool for data collection. Univariate, bivariate and multivariate analyses were performed to analyze the data. The data show that various socio-demographic characteristics are associated with the enrollment of HI. A significant statistical difference is seen between enrollment to HI and HH headship, age group of respondents, ability to feed the family, presence of chronic diseases in family, knowledge on HI, willingness to pay (WTP) for HI, having HI guidelines or books, participation in HI related training, interactions with neighbours, access to communication media: the radio/FM and TV, hoarding boards (HB), newspapers, posters/pamphlets/brochures; and access to health facilities. The results further show that female heads appear more likely to enroll (aOR = 1.47) in HI than the male. HH headship of the respondents also seem more likely to enroll. Higher age respondents are less likely to enroll. Interestingly, literate respondents and joint families are less likely to enroll than illiterate and nuclear families respectively. However, respondents having knowledge in HI seem more likely to enroll (aOR = 28.97, p1.673, p
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".