Willingness to pay for the social health insurance in Iran
Bibliographic record
Abstract
OBJECTIVE: The substantial level of out-of-pocket expenditure for health care by the population causes policy makers to draw particular attention to the proposal of a social health insurance for uninsured members of the community. Hence, it is essential to gather reliable information about the amount of Willingness To Pay (WTP) for health insurance. We assessed the WTP for health insurance in Iran in order to suggest an affordable social health insurance. METHODS: The study sample included 300 household heads in all Iranian provinces. The double bounded dichotomous choice approach was used to elicit the WTP. RESULTS: The average WTP for social health insurance per person per month was 137 000 Rial (5.5 $US). Household heads with higher levels of education, income and those who worked had more WTP for the health insurance. Besides, the WTP increased in direct proportion to the number of insured members of each household and in inverse proportion to the family size. CONCLUSIONS: From a policy point of view, the WTP value can be used as a premium in a society. An important finding of this study is that although households' Willingness To Pay is not more than the total insurance premium, households are willing to pay more than the premium they ought to pay for health insurance coverage. That is, total insurance premium is 150 000 Rials and households ought to pay approximately half of this sum. This can afford policy makers the ideal opportunity to provide good insurance coverage for medical services according to the need of society.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".