Willingness to pay for health insurance and its associated factors among staff of local authorities in Petaling District, Selangor, 2016
Bibliographic record
Abstract
Introduction: Currently, Malaysia finds that to fund health care primarily through taxation is no longer sustainable. Health insurance is another option to finance health care in a country. However before any major restructuring is done, the preference of the general population on health insurance can be assessed by eliciting values using willingness to pay (WTP) method. The aim of this study is to determine the proportion of WTP for health insurance and its associated factors among staff of the local authorities in Petaling District. Methodology: This is a cross-sectional study conducted among the staff of local authorities in Petaling District using simple random sampling. A self-administered questionnaire was used to elicit respondents’ WTP for health insurance using the contingent valuation method by a double bounded dichotomous choice format and to determine the factors that affect the willingness of the respondents to pay for health insurance. Variables were described using frequency and percentage for categorical data and mean and standard deviations for continuous variables. Chi square test was then used to measure the associations. Results: Among the respondents, 61.1% were willing to pay for health insurance. Of those, 25.7% were willing to pay maximum of RM20 per head per month followed by 23.0% that were willing to pay RM40 per head per month. The range of their maximum WTP amount was RM5 up to RM200 per head per month or RM 22 to RM 880 per household per month. Factors that were significantly associated with WTP for health insurance were education (p = 0.004), monthly salary for respondents (p = 0.042); as well as knowledge questions on who will be allowed to join SHI (p = 0.002), difference in payment in terms of health status (p = 0.015), difference in payment for those unemployed (p = 0.03) and regarding the allowance to take additional coverage from private health insurances (p = 0.001). Conclusion: Majority of the respondents were willing to pay for health insurance. Level of education, monthly salary and knowledge regarding health insurance were the factors associated with WTP for health insurance.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".