The Political Economy of the Mental Health System: A Marxist Analysis

The present paper analyses the functions of the mental health system in relation to the economic organisation of society, using concepts derived from Marx’s work on political economy and building on previous critiques. The analysis starts from the position that mental health problems are not equivalent to physical, medical conditions and are more fruitfully viewed as problems of communities or societies. Using the example of the United Kingdom, it traces how a public mental health system evolved alongside capitalism in order to manage the problems posed by people whose behaviour was too chaotic, disruptive or inefficient to participate in a labour market based on exploitation. The system provided a mixture of care and control, and under recent, Neoliberal regimes, these functions have been increasingly transferred to the private sector and provided in a capitalistic manner. Welfare payments are also part of the system and support those less seriously affected but unable to work productively enough to generate surplus value and profit. The increased intensity and precarity of work under Neoliberalism has driven up benefit claims at the same time as the Neoliberal state is trying to reduce them. These social responses are legitimised by the idea that mental disorders are medical conditions, and this idea also has a hegemonic function by construing the adverse consequences of social and economic structures as individual problems, an approach that has been particularly important during the rise of Neoliberalism. The concept of mental illness has a strategic role in modern societies, therefore, enabling certain contentious social activities by obscuring their political nature, and diverting attention from the failings of the underlying economic system. The analysis suggests the medical view is driven by political imperatives rather than science and reveals the need for a system that is more transparent and democratic. While the mental health system has some consistent functions across all modern societies, this account highlights one of the endemic contradictions of the capitalist system in the way that it marginalises large groups of people by narrowing the opportunities to make an economic contribution to society.

Motivated moral judgments about freedom of speech are constrained by a need to appear consistent

Speech is a critical means of negotiating political, adaptive interests in human society. Prior research on motivated political cognition has found that support for freedom of speech depends on whether one agrees with its ideological content. However, it remains unclear if people (A) openly hold that some speech should be more free than other speech; or (B) want to appear as if speech content does not affect their judgments. Here, we find support for (B) over (A), using social dominance orientation and political alignment to predict support for speech. Study 1 demonstrates that if people have previously judged restrictions of speech which they oppose, they are less harsh in condemning restrictions of speech which they support, and vice versa. Study 2 finds that when participants judge two versions of the same scenario, with only the ideological direction of speech being reversed, their answers are strongly affected by the ordering of conditions: While the first judgment is made in accordance with one’s political attitudes, the second opposing judgment is made so as to remain consistent with the first. Study 3 is a preregistered replication and elaboration on Study 2. Study 4 suggests that this effort to appear consistent functions to oblige the opposition to (also) protect one's own speech: We find that support for equal protection of all speech is stronger if a member of the opposition proposes it, rather than a member of one's own political coalition. These results are consistent with notions of an evolutionary arms race of social manipulation, and suggest that although people selectively endorse universal moral principles depending on their own political agenda, they conceal their bias from others and, perhaps, themselves.

Dynamically characterizing individual clinical change by the steady state of disease-associated pathway

Background Along with the development of precision medicine, individual heterogeneity is attracting more and more attentions in clinical research and application. Although the biomolecular reaction seems to be some various when different individuals suffer a same disease (e.g. virus infection), the final pathogen outcomes of individuals always can be mainly described by two categories in clinics, i.e. symptomatic and asymptomatic. Thus, it is still a great challenge to characterize the individual specific intrinsic regulatory convergence during dynamic gene regulation and expression. Except for individual heterogeneity, the sampling time also increase the expression diversity, so that, the capture of similar steady biological state is a key to characterize individual dynamic biological processes. Results Assuming the similar biological functions (e.g. pathways) should be suitable to detect consistent functions rather than chaotic genes, we design and implement a new computational framework (ABP: Attractor analysis of Boolean network of Pathway). ABP aims to identify the dynamic phenotype associated pathways in a state-transition manner, using the network attractor to model and quantify the steady pathway states characterizing the final steady biological sate of individuals (e.g. normal or disease). By analyzing multiple temporal gene expression datasets of virus infections, ABP has shown its effectiveness on identifying key pathways associated with phenotype change; inferring the consensus functional cascade among key pathways; and grouping pathway activity states corresponding to disease states. Conclusions Collectively, ABP can detect key pathways and infer their consensus functional cascade during dynamical process (e.g. virus infection), and can also categorize individuals with disease state well, which is helpful for disease classification and prediction.