Supported decision making: a review of the international literature

Supported decision making (SDM) refers to the process of supporting people, whose decision making ability may be impaired, to make decisions and so promote autonomy and prevent the need for substitute decision making. There have been developments in SDM but mainly in the areas of intellectual disabilities and end-of-life care rather than in mental health. The main aim of this review was to provide an overview of the available evidence relevant to SDM and so facilitate discussion of how this aspect of law, policy and practice may be further developed in mental health services. The method used for this review was a Rapid Evidence Assessment which involved: developing appropriate search strategies; searching relevant databases and grey literature; then assessing, including and reviewing relevant studies. Included studies were grouped into four main themes: studies reporting stakeholders’ views on SDM; studies identifying barriers to the implementation of SDM; studies highlighting ways to improve implementation; and studies on the impact of SDM. The available evidence on implementation and impact, identified by this review, is limited but there are important rights-based, effectiveness and pragmatic arguments for further developing and researching SDM for people with mental health problems.

Analogies to semantic and syntactic mental labour in the Feist decision

The Supreme Court of the United States in Feist v. Rural (Feist, 1991) specified that compilations or databases, and other works, must have a minimal degree of creativity to be copyrightable. The significance and global diffusion of the decision is only matched by the difficulties it has posed for interpretation. The judgment does not specify what is to be understood by creativity, although it does give a full account of the negative of creativity, as ‘so mechanical or routine as to require no creativity whatsoever’ (Feist, 1991, p.362). The negative of creativity as highly mechanical has particularly diffused globally.

A recent interpretation has correlated ‘so mechanical’ (Feist, 1991) with an automatic mechanical procedure or computational process, using a rigorous exegesis fully to correlate the two uses of mechanical. The negative of creativity is then understood as an automatic computation and as a highly routine process. Creativity is itself is conversely understood as non-computational activity, above a certain level of routinicity (Warner, 2013).

The distinction between the negative of creativity and creativity is strongly analogous to an independently developed distinction between forms of mental labour, between semantic and syntactic labour. Semantic labour is understood as human labour motivated by considerations of meaning and syntactic labour as concerned solely with patterns. Semantic labour is distinctively human while syntactic labour can be directly humanly conducted or delegated to machine, as an automatic computational process (Warner, 2005; 2010, pp.33-41).

The value of the analogy is to greatly increase the intersubjective scope of the distinction between semantic and syntactic mental labour. The global diffusion of the standard for extreme absence of copyrightability embodied in the judgment also indicates the possibility that the distinction fully captures the current transformation in the distribution of mental labour, where syntactic tasks which were previously humanly performed are now increasingly conducted by machine.

The paper has substantive and methodological relevance to the conference themes. Substantively, it is concerned with human creativity, with rationality as not reducible to computation, and has relevance to the language myth, through its indirect endorsement of a non-computable or not mechanical semantics. These themes are supported by the underlying idea of technology as a human construction. Methodologically, it is rooted in the humanities and conducts critical thinking through exegesis and empirically tested theoretical development

References

Feist. (1991). Feist Publications, Inc. v. Rural Tel. Service Co., Inc. 499 U.S. 340.

Warner, J. (2005). Labor in information systems. Annual Review of Information Science and Technology. 39, 2005, pp.551-573.

Warner, J. (2010). Human Information Retrieval (History and Foundations of Information Science Series). Cambridge, MA: MIT Press.

Warner, J. (2013). Creativity for Feist. Journal of the American Society for Information Science and Technology. 64, 6, 2013, pp.1173-1192.

Unravelling the migration decision-making process: English early retirees moving to rural mid-Wales

There are established migrant reasons to explain rural in-migration. These include quality of life, rural idyll and lifestyle motivations. However, such one-dimensional sound bites portray rural in-migration in overly simplistic and stereotypical terms. In contrast, this paper distinguishes the decision to move from the reason for moving and in doing so sheds new light on the interconnections between different domains (family, work, finance, health) of the migrant's life which contribute to migration behaviour. Focussing on early retirees to mid-Wales and adopting a life course perspective the overall decision to move is disaggregated into a series of decisions. Giving voices to the migrants themselves demonstrates the combination of life events necessary to lead to migration behaviour, the variable factors (and often economic dominance) considered in the choice of destination (including that many are reluctant migrants to Wales), and the perceived 'accidental' choice of location and/or property. It is argued that quality of life, rural idyll and lifestyle sound bites offer an inadequate understanding of rural in-migration and associated decision-making processes. Moreover, they disguise the true nature of migrant decision making.

Shared decision making in patients at risk of cancer: The role of domain and numeracy

Background
Shared decision making has become an integral part of medical consultation. Research has, however, reported wide differences in individuals' desires to be involved in the decision-making process, and these differences in preferences are likely to be the result of a number of factors including age, education and numeracy.

Objective
To investigate whether patients at genetic risk for cancer had preferences for shared decision making that differed depending on medical domain (general health vs. cancer) and whether decision preferences are linked to numeracy abilities.

Methods
Four hundred and seventy-six women who consented to participate in response to an email sent by a local branch of the U.S.-based Cancer Genetics Network (CGN) to its members. Participants completed the Control Preference Scale, as well as an objective and subjective numeracy scales.

Results
Decision domain (cancer vs. general health) was not associated with women's preferences for involvement in decision making. Objective and subjective numeracy predicted a preference for decision involvement in general, and only objective numeracy was predictive with regard to cancer.

Conclusion
Participants were equally likely to state they wanted to play an active, collaborative or passive role in both medical domains (general and cancer). High-numeracy participants were more likely to express a desire for an active role in general and in case they were diagnosed with cancer.

Practice implications
Health authorities' recommendations to clinicians to include patients in their medical decisions are supported by patients' desires, and clinicians should be cognizant of their patients' preferences as well as their numeracy skills.

An Extended Event Reasoning Framework for Decision Support under Uncertainty

To provide in-time reactions to a large volume of surveil- lance data, uncertainty-enabled event reasoning frameworks for CCTV and sensor based intelligent surveillance system have been integrated to model and infer events of interest. However, most of the existing works do not consider decision making under uncertainty which is important for surveillance operators. In this paper, we extend an event reasoning framework for decision support, which enables our framework to predict, rank and alarm threats from multiple heterogeneous sources.

An Adaptive Machine Learning Decision System for Flexible Predictive Maintenance

Process monitoring and Predictive Maintenance (PdM) are gaining increasing attention in most manufacturing environments as a means of reducing maintenance related costs and downtime. This is especially true in industries that are data intensive such as semiconductor manufacturing. In this paper an adaptive PdM based flexible maintenance scheduling decision support system, which pays particular attention to associated opportunity and risk costs, is presented. The proposed system, which employs Machine Learning and regularized regression methods, exploits new information as it becomes available from newly processed components to refine remaining useful life estimates and associated costs and risks. The system has been validated on a real industrial dataset related to an Ion Beam Etching process for semiconductor manufacturing.