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The aim of the Structural Biology Extensible Visualization Scripting Language (SBEVSL) project is to allow users who are experts in one scripting language to use that language in a second molecular visualization environment without requiring the user to learn a new scripting language. ConSCRIPT, the first SBEVSL release, is a plug-in for PyMOL that accepts RasMol scripting commands either as premade scripts or as line-by-line entries from PyMOL's own command line. The plug-in is available for download at http://sourceforge.net/projects/sbevsl/files in the ConSCRIPT folder.

Background: The government of Sierra Leone introduced Social Health Insurance Scheme as a measure to remove financial barriers that beset the people in accessing health to ensure universal coverage. Under this policy, the citizens were encouraged to subscribe to the scheme to avoid out of pocket payment for healthcare at the point of use. This study was conducted to find out the predictors of willingness among the people to pay for health insurance premium. Methods: A cross-sectional study design was employed in six selected districts in Sierra Leone. Quantitative data was collected for this study through the use of semi-structured questionnaire with a sample size of 1185 respondents. Data was analysed into descriptive and inferential statistics using the contingent valuation model. Statistical analysis was run at 5% significant level using Stata version 14.0 software. Results: The results showed that majority of the respondent are willing to join and pay a monthly premium of Le 10 000 (US$1.03) with an estimated mean contribution of about Le 14 089 (US$1.44) and the top five predictors of willingness to pay (WTP) were household monthly income, age, district of resident, gender, and educational qualification. Conclusion:The findings on predictors of WTP premium of Sierra Leone National Social Health Insurance (SLeNSHI), suggests that the socio-demographic characteristics of the population are important in premium design and payment. Efforts at improving the socio-economic statuses of the population could be helpful in premium design and payment.

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