IGLOO: An integrated framework for sustainable return to work in workers with common mental disorders

ABSTRACT

Current research on return to work (RTW) for employees with common mental disorders suffers from two limitations. First, research mostly focuses on the influence of resources during the absence period ignoring the resources which may facilitate sustainable RTW, i.e. employees continuing to work and thrive at work post-return. Second, research tends to view the work and non-work domains separately and fails to consider the interaction of resources at the individual, group, leader and organisational levels, once back at work. In the present position paper, we present an integrated framework and a preliminary definition of sustainable RTW. Based on current occupational health psychology theory and existing research on RTW, we develop ten propositions for the resources in and outside work, which may promote sustainable RTW. In addition to the individual, group, leader, and organisational levels, we also argue for the importance of the overarching context, i.e. the societal context and the culture and legislation that may promote sustainable RTW. Our framework raises new questions that need to be addressed to enhance our understanding of how key stakeholders can support employees with common mental health disorders staying and thriving at work.     

On Causal Inferences for Personalized Medicine: How Hidden Causal Assumptions Led to Erroneous Causal Claims About the D-Value

Abstract

Personalized medicine asks if a new treatment will help a particular patient, rather than if it improves the average response in a population. Without a causal model to distinguish these questions, interpretational mistakes arise. These mistakes are seen in an article by Demidenko that recommends the “D-value,” which is the probability that a randomly chosen person from the new-treatment group has a higher value for the outcome than a randomly chosen person from the control-treatment group. The abstract states “The D-value has a clear interpretation as the proportion of patients who get worse after the treatment” with similar assertions appearing later. We show these statements are incorrect because they require assumptions about the potential outcomes which are neither testable in randomized experiments nor plausible in general. The D-value will not equal the proportion of patients who get worse after treatment if (as expected) those outcomes are correlated. Independence of potential outcomes is unrealistic and eliminates any personalized treatment effects; with dependence, the D-value can even imply treatment is better than control even though most patients are harmed by the treatment. Thus, D-values are misleading for personalized medicine. To prevent misunderstandings, we advise incorporating causal models into basic statistics education.     

When Opportunity Knocks: Confronting Theory and Empirics About Dynamics of Gender Wage Inequality

Social Indicators Research - We present empirical evidence that large structural shocks are followed by changes in labor market inequality. Specifically, we study short-run fluctuations in adjusted...     

Perception and Acceptance of Technological and Environmental Risks: Why Are Poor Countries Less Concerned?

The research has been aimed at answering two questions: (1) What factors impact perception and acceptance of technological and environmental hazards? (2) Why are rich societies involved more in protecting their environment and health than poor societies? Data has been collected from representative samples of two countries—Poland and Sweden. The results indicate that (1) contrary to earlier findings, the inverse relations between perceived benefits and dangers of hazards has not been observed, (2) acceptance of a risk has been mostly influenced by perceived benefits, (3) rejection of a risk has been mostly influenced by its perceived harmful consequences. Concerning the second question, it has been found that: (1) perceived hazard's danger and benefit is not the only factor that impacts its acceptance, and (2) a broader economic context can impact acceptance (tolerance) of hazards. It has been found that being aware of high dangers and not very high benefits of hazardous activities, Poles still have accepted them. Thus, Poles seem to follow an old proverb: “When one does not have what one likes, one has to like what one has.”     

Accounting for ethos or programmes for conduct? The brave new world of research ethics committees

This paper examines the operation of power and its consequences arising from the growth of new ethical bureaucracies in universities. We use the UK as a case study to illustrate more general points about the globalised nature and impact of such bureaucratisation. Our focus is on the social sciences as this is where, we argue, the impact is likely to be most marked. The paper is organised in five sections. The first introduces our concerns. Section 2 traces the genealogy of these new regimes of control in the UK. We then problematise the new ethical bureaucracies, making an analysis in terms of the shift in the locus of power away from researchers to becoming centralised in bureaucratic structures. In section 4 we explore some of the ways in which researchers might respond to the changing regimes of ethical control. Finally, we offer considerations of the ways in which ethical governance of research might be differently conducted so as to avoid the adverse consequences of new regimes of control on research practice. Our aim is to provoke debate and thereby contribute to a platform from which to reassert ways to ensure that research is ethical and that do not interfere with the production and consumption of critical social science.     

Identifiable state‐space models: A case study of the Bay of Fundy sea scallop fishery

State‐space models (SSMs) are now popular tools in fisheries science for providing management advice when faced with noisy survey and commercial fishery data. Such models are often fitted within a Bayesian framework requiring both the specification of prior distributions for model parameters and simulation‐based approaches for inference. Here we present a frequentist framework as a viable alternative and recommend using the Laplace approximation with automatic differentiation, as implemented in the R package Template Model Builder, for fast fitting and reliable inference. Additionally we highlight some identifiability issues associated with SSMs that fisheries scientists should be aware of and demonstrate how our modelling strategy surmounts these problems. Using the Bay of Fundy sea scallop fishery we show that our implementation yields more conservative advice than that of the reference model. The Canadian Journal of Statistics 47: 27–45; 2019 © 2018 Statistical Society of Canada     

Post-Truth and the Production of Ignorance

This essay is written in response to Fujimura and Holmes’s piece “Staying the Course,” published in the December 2019 special issue of Sociological Forum—Resistance in the Twenty-First Century.     

Research encounters,reflexivity and supervision

Reflexivity in qualitative and ethnographic social science research can provide a rich source of data, especially regarding the affective, performative and relational aspects of interviews with research subjects. This paper explores by means of three case examples different ways of accessing and using such reflexivity. The examples are drawn from an empirical psycho-social study into the identity transitions of first-time mothers in an inner-city multicultural environment. Fieldnotes and supervision were used to engage with researcher subjectivity, to enhance the productive use of reflexivity and to address the emotional work of research. The methodology of the supervision was psychoanalytic, in its use of a boundaried frame and of psychoanalytic forms of noticing oneself, of staying engaged emotionally as well as creating a reflective distance. The examples illustrate how this can enhance the knowledge gained about the research subjects.