Procrastination is common among students, with prevalence estimates double or even triple those of the working population. This inflated prevalence indicates that the academic environment may appear as "procrastination friendly" to students. In the present paper, we identify social, cultural, organizational, and contextual factors that may foster or facilitate procrastination (such as large degree of freedom in the study situation, long deadlines, and temptations and distractions), document their research basis, and provide recommendations for changes in these factors to reduce and prevent procrastination. We argue that increased attention to such procrastination-friendly factors in academic environments is important and that relatively minor measures to reduce their detrimental effects may have substantial benefits for students, institutions, and society.

Procrastination is related to unhealthy personal financial behaviors, such as postponing retirement savings, last minute shopping, and not paying bills on time. The present paper explores factors that could explain why procrastinators demonstrate more financial problems compared to non-procrastinators. Study 1 ( N = 675) focused on planning, as both procrastination and poor financial habits are negatively related to planning. Results confirmed that procrastination was a significant predictor of personal finances, but the propensity to plan was not. Study 2 ( N = 500) explored the roles of procrastination and financial self-efficacy in two facets of financial behavior, financial impulsivity and financial planning. Results indicated that the effect of procrastination on financial behavior was fully mediated by financial self-efficacy. Hence, these results suggest that procrastination operates primarily through its self-efficacy component to impact financial behavior negatively.

Purpose: To develop mapping algorithms that transform Diabetes-39 (D-39) scores onto EQ-5D-5L utility values for each of eight recently published country-specific EQ-5D-5L value sets, and to compare mapping functions across the EQ-5D-5L value sets.Methods: Data include 924 individuals with self-reported diabetes from six countries. The D-39 dimensions, age and gender were used as potential predictors for EQ-5D-5L utilities, which were scored using value sets from eight countries (England, Netherland, Spain, Canada, Uruguay, China, Japan and Korea). Ordinary least squares, generalized linear model, beta binomial regression, fractional regression, MM-estimation, and censored least absolute deviation were used to estimate the mapping algorithms. The optimal algorithm for each country-specific value set was primarily selected based normalized root mean square error (NRMSE), normalized mean absolute error (NMAE) and adjusted-r 2 . Cross-validation with 5-fold approach was conducted to test the generalizability of each model. Results:The fractional regression model with loglog as a link function consistently performed best in all country-specific value sets. For instance, the NRMSE (0.1282) and NMAE (0.0914) were the lowest, while adjusted-r 2 was the highest (52.5%) when the English value set was considered. Among D-39 dimensions, the energy and mobility was the only one that was consistently significant for all models. Conclusions:The D-39 can be mapped onto the EQ-5D-5L utilities with good predictive accuracy. The fractional regression model, which is appropriate for handling bounded outcomes, outperformed other candidate methods in all country-specific value sets. However, the regression coefficients differed reflecting preference heterogeneity across countries.

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2 AbstractPurpose Different health state utility (HSU) instruments produce different utilities for the same individuals, thereby compromising the intended comparability of economic evaluations of health care interventions. When developing crosswalks, previous studies have indicated non-linear relationships. This paper inquires into the degree of non-linearity across the four most widely used HSUinstruments, and proposes exchange rates that differ depending on the severity levels of the health state utility scale.Method Overall, 7933 respondents from six countries; 1760 in a non-diagnosed healthy group and 6173 in seven disease groups, reported their health states using four different instruments: EQ-5D-5L, SF-6D, HUI-3 and 15D. Quantile regressions investigate the degree of non-linear relationships between these instruments. To compare the instruments across different disease severities, we split the health state utility scale into utility intervals with 0.2 successive decrements in utility starting from perfect health at 1.00. Exchange Rates (ERs) are calculated as the mean utility difference between two utility intervals on one HSU-instrument divided by the difference in mean utility on another HSU-instrument.Result Quantile regressions reveal significant non-linear relationships across all four HSUinstruments. The degrees of non-linearities differ, with a maximum degree of difference in the coefficients along the health state utility scale of 3.34 when SF-6D is regressed on EQ-5D. At the lower end of the health state utility scale, the exchange rate from SF-6D to EQ-5D is 2.19, whilst at the upper end it is 0.35. ConclusionComparisons at different utility levels illustrates the fallacy of using linear functions as crosswalks between HSU-instruments. The existence of non-linear relationships between different HSU-instruments suggests that level-specific exchange rates should be used when converting a change in utility on the instrument used, onto a corresponding utility change had another instrument 3 been used. Accounting for non-linearities will increase the validity of the comparison for decision makers when faced with a choice between interventions whose calculations of QALY-gains have been based on different HSU-instruments.