complexity theory

COVID-19 has rapidly brought many changes to people’s lives. The current research aimed to explore how these changes affected the social resilience of the residents in a local neighborhood. 

  The present case study was conducted in the spring of 2021, using in-depth interviews and field observations in the Tawheed neighborhood of Gonabad City, Iran. Purposive sampling was used to select the interviewees with maximum variation. Interviews continued until data saturation was reached, which occurred after the 17th participant was interviewed. To analyze the data, a directed qualitative content analysis and the complexity approach proposed by David Byrne and Gil Callahan were utilized. 

The local residents’ social resilience during the pandemic was explained in light of the complexity theory. A total number of four categories and 13 sub-categories were extracted: uncertainty of the disease (uncertainty of information about the disease, no common understanding of the disease, no common norms of health behaviors, and unknown aspects of the disease), features of connectivity and network of social relationships (perceived common benefits, family cohesion, access to online social networks), initial states and availability of sources (unstable living conditions, poor health facilities, and structure of the neighborhood), interaction of micro- and macro-factors (trust in healthcare systems and leaders, and financial rules and policies). 

  The social resilience of the residents in the local neighborhood during the pandemic was influenced by many factors, primarily related to their initial conditions. To improve social resilience, it seems necessary to carry out effective interventions based on local and indigenous capacities.

In implementation science, implementation has been widely theorized and assessed. Context, on the other hand, usually played a minor role in the field and was usually conceptualized in a rather positivist way. Despite some promising efforts, there is a strong need to continue building theory on context and operationalizing the concept in implementation practice. I argue for the benefit of integrating complexity theory into our understanding of context in order to further our thinking about context and intervention as a system. This should be reflected by the way in which we build theory as well as apply this theory by employing methods that adequately account for complexity in systems.

The purpose of this review is to establish how the theory of complexity is understood and instituted in dentistry.

The stomatognathic system can be understood as a dynamic, complex, and adaptive system. Each pathological or physiological condition of this system involves physical, chemical, and biological processes in constant, open, and changing interactions with social, emotional, nutritional, political, and economic processes. Against this background, specifically, the following research questions were posed: How do researchers use complexity science in dentistry? How is complexity science described in dentistry articles?.

A brief literature search was implemented, which identified 11 PubMed articles as well as two from the Literatura Latinoamericana y del Caribe en Ciencias de la Salud (LILACS) database and one from the Cochrane Library for a full text review. Studies on complexity in the dental sciences are mostly presented in the form of critical opinion articles, which corresponded to 50% of the reviewed articles. In dentistry, complexity is understood as less of a theory and more as a line of thinking regarding procedures that can become complex at any given time.

This article shows that there are great difficulties in integrating complexity and understanding it in dentistry. There are many aspects from complexity science that still need to be understood in oral health.

The re-conceptualization of knowledge translation (KT) in Kitson and colleagues’ manuscript “Using Complexity and Network Concepts to Inform Healthcare Knowledge Translation” is an advancement in how one can incorporate implementation into the KT process. Kitson notes that “the challenge is to explain how it might help in the healthcare policy, practice, and research communities.” We propose that these concepts are well presented when considering highly-partnered research that includes all sectors. In this manuscript we provide an example of highly-partnered KT effort framed within the KT Complexity Network Theory. This effort is described by identifying the activities and sectors involved.

A more sophisticated understanding of the unpredictable, disorderly and unstable aspects of healthcare organisations is developing in the knowledge translation (KT) literature. In an article published in this journal, Kitson et al introduced a new model for KT in healthcare based on complexity theory. The Knowledge Translation Complexity Network Model (KTCNM) provides a fresh perspective by making the complexity inherent in complex systems overt. The model encourages a whole system view and focuses on the interdependent relationships between actions, interactions and actors. Taking a systems approach assists our understanding of the connections, communication and collaboration necessary to promote knowledge mobilisation and facilitate the adoption of change. With further development, this could enable the targeting of more effective strategies across the various stakeholders and levels of service, fostering redesign and innovation.

This commentary addresses two points raised by Kitson and colleagues’ article. First, increasing interest in applying the Complexity Theory lens in healthcare needs further systematic work to create some commonality between concepts used. Second, our need to adopt a better understanding of how these systems organise so we can change the systems overall behaviour, creates a paradox. We seek to manipulate systems that self-organise and follow their own internal rules. Although, our actions may impact and indeed meet some of our objectives, system behaviour will always emerge with unpredictable consequences. Likewise, outcomes at the aggregated level of the system never reaches an optimal point as defined by the ‘external controller.’ Kitson and colleagues’ theoretical model may struggle to resolve the paradox of gaining control over the multiple knowledge translation (KT) systems covered by the model, because theoretically these systems retain control under the principle of self-organisation. That is not to suggest that individual agents cannot influence system dynamics just that the desired outcome cannot be guaranteed. Indeed, for systems to change they will need strong incentives.