Reasons for substance use continuation and discontinuation during pregnancy: A qualitative study

Background

Substance use during pregnancy is a major public health concern, stemming from potential physical and psychosocial harms to both the mother and child.

Migration and Intergenerational Replacement in Europe

There are long‐standing concerns over low fertility levels in Europe and an increasingly important debate on the extent to which migration can compensate for below‐replacement fertility. To inform this debate, a wide array of indicators have been developed to assess the joint influence of fertility, mortality, and migration on birth replacement and intergenerational replacement. These indicators are based on various models and assumptions and some are particularly data demanding. In this article we propose a simple method to assess how far migration alters the extent of replacement for a birth cohort as it ages. We term the measure the overall replacement ratio (ORR). It is calculated by taking the size of a female birth cohort at selected ages divided by the average size of the cohorts of mothers in the year of birth. We present estimates of the ORR for a range of European countries representing different replacement regimes. We demonstrate that for many countries net migration has become a key factor in their population trends during the last few decades.     

Data Envelopment Analysis for Composite Indicators: A Multiple Layer Model

The development of a composite indicator (CI) over a set of individual indicators is worthwhile in case the methodological aggregation process is sound and the results are clear. It can then be used as a powerful tool for performance evaluation, benchmarking, and decision making. In this respect, data envelopment analysis (DEA), as a self appraisal technique, has recently received considerable attention in the construction of CIs for policy analysis and public communication. However, due to the ever increasing complexity of numerous performance evaluation problems, more and more potential indicators might be developed to represent an evaluation activity in a more comprehensive way. These indicators might also belong to different categories and further be linked to one another constituting a multilayer hierarchical structure. Simply treating all the indicators to be in the same layer as is the case in the basic DEA model thereby ignores the information on their hierarchical structure, and further leads up to weak discriminating power and unrealistic weight allocations. To overcome this limitation, a multiple layer DEA-based CI model is developed in this study to embody a hierarchical structure of indicators in the DEA framework, and both its primal and dual form are realized. The proposed model is illustrated by constructing a composite road safety performance index for a set of European countries.     

Towards a behavioral theory of systemic hypothesis-testing and the error of the third kind

Scientific ideas neither arise nor develop in a vacuum. They are always nutured against a background of prior, partially conflicting ideas. Systemic hypothesistesting is the problem of testing scientific hypotheses relative to various systems of background knowledge. This paper shows how the problem of systemic hypothesis-testing (Sys HT) can be systematically expressed as a constrained maximimization problem. It is also shown how the error of the third kind (E _III) is fundamental to the theory of Sys HT.The error of the third kind is defined as the probability of having solved the ‘wrong’ problem when one should have solved the ‘right’ problem. This paper shows howE _III can be given both a systematic as well as a systemic treatment. Sys HT gives rise to a whole host of new decision problems, puzzles, and paradoxes.     

Fish for Dinner? Balancing Risks,Benefits, and Values in Formulating Food Consumption Advice

Many and complex factors underlie seemingly simple decisions about what to eat. This is particularly so for foods such as fish, which present consumers with both risks and benefits. Advice about what type of and how much fish to consume is abundant, but that advice is often confusing or contradictory, reflecting the differing mandates and orientations of those advising. We survey a range of issues that can and should be incorporated into dietary advice, and offer tools for health agencies tasked with providing it. We argue that risks and benefits should not be limited to direct physical health. Rather, socioeconomic and community factors, unintended or indirect effects, and nonhuman‐health outcomes such as animal welfare and planetary health should also be considered and weighed. We provide examples of existing fish consumption guidance to highlight the conflicting messages that emerge when different sources of advice with singular aims of avoiding risk, gaining nutritional benefit, or sustaining fish populations are juxtaposed. We then offer tools borrowed from health and other fields to guide health agencies toward developing more comprehensive advice and targeting that advice for specific populations.     

Scientization Under Pressure—The Problematic Role of Expert Bodies During the Handling of the COVID-19 Pandemic

Public Organization Review - This article focuses on the role of experts in the Norwegian decision-making process in central government during the crisis management of the COVID-19 pandemic. It is...     

On the Measurement of Corporatism

ABSTRACT: It is noticed that in both theoretical and empirical analyses concerning the relationship between corporatism and economic performance, the role of government is kept rather implicit. This article aims to incorporate government into the analysis in a more explicit way by analyzing whether government policies differ between corporatist and non-corporatist countries. The empirical results of the research reveal that corporatist countries are not only characterized by centralization or coordination of wage bargaining, but also by relatively high government expenditure, tax revenues, a higher ratio of active to passive labour market policies and also higher average unemployment benefits.     

Daisee: Adaptive importance sampling by balancing exploration and exploitation

We study adaptive importance sampling (AIS) as an online learning problem and argue for the importance of the trade-off between exploration and exploitation in this adaptation. Borrowing ideas from the online learning literature, we propose Daisee, a partition-based AIS algorithm. We further introduce a notion of regret for AIS and show that Daisee has $𝒪(\sqrt{T}{(\log T)}^{\frac{3}{4}})$ cumulative pseudo-regret, where $T$ is the number of iterations. We then extend Daisee to adaptively learn a hierarchical partitioning of the sample space for more efficient sampling and confirm the performance of both algorithms empirically.