The use of computer models offers a general and flexible framework that can help to deal with some of the complexities and difficulties associated with the development of water management plans as prescribed by the Water Framework Directive. However, despite the advantages modelling presents, the integration of information derived from models into policy is far away from being trivial or the norm. Part of the difficulties of this integration is rooted in the lack of confidence policy makers have on the incorporation of modelling information into policy formulation. In this paper we examine the reasons for this apparent lack of confidence and explore how some tools, presently in use, address this problem. We conclude that public confidence in models is highly dependent on the way uncertainties are addressed and suggest possible directions of action to improve the current situation. Four real case studies illustrate how computer models have been used in The Netherlands for carrying out management plans at regional and national scale. We suggest that the solution to integrate modelling information into policy formulation lies on both the modelling and the policy-making communities.
Background
Burnout is common among university students and may adversely affect academic performance. Little is known about the true burden of this preventable malady among university students in low-and-middle-income countries (LMICs).
Objectives
This study aimed to systematically estimate the prevalence of burnout among university students in LMICs.
Methods
We searched PubMed, Google Scholar, CINAHL, Web of Science, African Journals Online, and Embase from the inception of each database until February 2021. Original studies were included. No study design or language restrictions were applied. A random-effects meta-analysis was performed using STATA version 16.0. Heterogeneity and publication bias were assessed using Q-statistics and funnel plots, respectively.
Results
Fifty-five unique articles, including a total of 27,940 (Female: 16,215, 58.0%) university students from 24 LMICs were included. The Maslach Burnout Inventory (MBI) was used in 43 studies (78.2%). The pooled prevalence of burnout was 12.1% (95% Confidence Interval (CI) 11.9–12.3; I2 = 99.7%, Q = 21,464.1, p = < 0.001). The pooled prevalence of emotional exhaustion (feelings of energy depletion), cynicism (negativism), and reduced professional efficacy were, 27.8% (95% CI 27.4–28.3; I2 = 98.17%. p = <0.001), 32.6 (95% CI: 32.0–33.1; I2: 99.5%; p = < 0.001), and 29.9% (95% CI: 28.8–30.9; I2: 98.1%; p = < 0.001), respectively.
Conclusion
Nearly one-third of university students in LMICs experience burnout. More studies are needed to understand the causes of burnout in this key population. There is a need to validate freely available tools for use in these countries.
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