Sharing mental models is critical for interdisciplinary collaboration

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Community member post by Jen Badham and Gabriele Bammer

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Jen Badham (biography)

What is a mental model? How do mental models influence interdisciplinary collaboration? What processes can help tease out differences in mental models?

Mental models

Let’s start with mental models. What does the word ‘chair’ mean to you? Do you have an image of a chair, perhaps a wooden chair with four legs and a back, an office chair with wheels, or possibly a comfortable lounge chair from which you watch television? Maybe you do not have an image at all, but instead have a collection of associations such as ‘sit’ and ‘relax’ or ‘working at computer’ that together provide a definition.

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Gabriele Bammer (biography)

However you think about the word ‘chair’, you will have some central concept that allows you to categorise something as a chair even if it differs from every other chair you have previously seen. The full picture of your experiences with chairs is abstracted to include only a small number of key characteristics and each of those is idealised or simplified. The central concept that emerges is a mental model that is applied when faced with a new potential chair. Mental models apply, for instance, for concrete objects like ‘chair’, abstract concepts like ‘trust’, and geographical locations like ‘Sydney’.

However, each person’s experiences are unique and they may attach different importance to particular features. Mental models can therefore differ. For example, let us explore further the concept of ‘chair’. A person who considers shape to be important may exclude beanbags and perhaps stools from the category of ‘chair’, but these may both be included by a person who uses the function of providing something to sit upon as the defining characteristic.

A useful exercise to illustrate this point is to present pictures of many different items that can be sat on, including other furniture and sculptures, and ask people to raise their hands if they consider the item to be a chair. Disagreements always provoke lively discussion.

Personal mental models contribute to a common language, allowing two or more people to share and refine the general concept. For some concepts, such as ‘chair’, people tend to have similar experiences and there is substantial overlap in their mental models.

Mental models and interdisciplinarity

In research projects that cross disciplines, however, it is a common experience to suddenly realise that different team members are using the same terms in different ways. This is because each discipline builds up its own associations with a term that reflects the special interest of that discipline. Part of becoming a specialist involves being inculcated with particular mental models, the expert knowledge of that discipline.

For example, an economist thinks of ‘values’ as benefits that may be gained from goods or services, a mathematician as numbers calculated for a variable and a philosopher as ethical principles.

There is even greater potential for confusion when it is not just terminology that is being used in different ways, but instead different concepts of what is important in a complex system of many entities and the relationships among them.

Researchers and stakeholders have mental models about situations and aspects of the world they inhabit, capturing the features that are most important to them from their experiences and making causal or other connections from observed patterns or received knowledge. Those mental models underpin the attitudes and beliefs they bring to the examination of a complex system.

To take a simple example, ‘low rainfall’ may be associated with drought and economic hardship for agricultural scientists, good weather and profitability for tourism researchers, the need for increased irrigation, pressure on rivers and potential conflict for sustainability scientists and improved survival of some plant species over others with consequent effects on other aspects of the ecology for biodiversity specialists.

Only by communicating their mental models can people investigate the similarities and differences between what each of them has captured as important from their experiences. In an earlier blog post Deana Pennington also identified the importance of developing “external representations” of mental models.

Identifying differences in mental models

Jointly designing a diagrammatic, mathematical and other formal model of a system is a particularly effective way of drawing out differences in mental models of complex systems. Designing a model requires a thorough specification of how a model is to ‘work’, and the rigour of that design process reveals the mental models of the participants. Just as different responses to whether something is a chair generates discussion about how to define a chair, the process of describing the important elements of a system in detail and how they influence each other promotes discussion of different understandings of that system.

For some projects, such discussions about how to design the model may be more valuable than actually building the model. Indeed such discussions may lead to new shared understanding about the problem that may even stimulate new ideas about how to respond to it.

Have you had the experience of suddenly recognising that someone else was using a term in a relevantly different way? What sorts of learning came out of the discussion refining that term? What tools, methods or practices have you found effective for drawing out differences in mental models?

Biography: Jen Badham is a Research Fellow at the Centre for Public Health, Queen’s University Belfast, Northern Ireland. She has worked as both a modeller and policy advisor and is currently interested in the way in which social networks influence changes in behaviour. She is member of the Core Modeling Practices pursuit funded by the National Socio-Environmental Synthesis Center (SESYNC).

Biography: Gabriele Bammer is a professor at The Australian National University in the Research School of Population Health’s National Centre for Epidemiology and Population Health. She is developing the new discipline of Integration and Implementation Sciences (I2S) to improve research strengths for tackling complex real-world problems through synthesis of disciplinary and stakeholder knowledge, understanding and managing diverse unknowns and providing integrated research support for policy and practice change. She leads the theme “Building Resources for Complex, Action-Oriented Team Science” at the National Socio-Environmental Synthesis Center (SESYNC).

Co-producing research: Why we need to say what we mean, mean what we say, and learn as we go

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Community member post by Bev J. Holmes

Bev J. Holmes (biography)

The co-production or co-creation of research is not new – action based research traditions can lay claim to a long history, but are those of us involved in co-production doing enough to understand what it means?

In their work on public involvement, Antoine Boivin and colleagues (2014) note there is such widespread support for the rhetoric of co-production that we may dismiss (I would add not even acknowledge) the tensions that arise when professionals and lay people work together. Co-production in health research is similar. We need to work harder to say what we mean, mean what we say, and learn as we go.

Say what we mean

Co-production is easy enough to say, but what does it actually mean? I don’t often hear it defined, perhaps because it sounds obvious. When pushed, people may describe it as partnership or collaboration.

At a workshop at the 2017 Global Implementation Conference, we defined co-production as collaboration in governance, priority setting, conducting research and/or knowledge translation.

In turn, we defined collaboration according to the International Association of Public Participation’s public participation spectrum (PDF 152KB).

We noted that co-production involves researchers and others with a stake in the project: citizens, patients, health care providers, and/or health care decision- and policy-makers.

The important piece in all of the above is shared decision-making. But operationalizing the ‘co’ in co-production – a prefix implying joint, mutual, in common – is not an easy task. Is co-production the appropriate model for the project at hand? It’s not always: the other elements on the spectrum (informing, consulting, involving, empowering) are as valid, depending on the situation. But they mean different things. The message here is that definitions – or lack thereof – have significant implications for action, discussed next.

Mean what we say

Committing to co-creation – and all it implies with regard to shared decision-making – means acknowledging that co-production is challenging: it requires role clarity, attention to power imbalances, difficult discussions about research rigour versus research relevance, and constant monitoring (Holmes et al., 2016). It also means putting in place the mechanisms to support it.

Boivin and colleagues (2014) note three areas for attention:

  • Credibility: Participants need to learn each other’s language and be seen as valued and relevant sources of knowledge for each other.
  • Legitimacy: Participants need to be clear on whose behalf they speak (eg., people in the same profession, users of a particular service, patients with the same condition, employees in a specific organization) and be supported to do so.
  • Power: All participants must be able to influence decisions.

Practical steps can be taken in all these areas. For example credibility comes from participants’ experience and expertise, but can be built through access to additional information or skill-building. For legitimacy, Boivin and colleagues (2014) point out the difference between statistical representativeness of a group (correspondence between the descriptive characteristics of a sample and those of the population from which they are drawn) and representation. The former is difficult and expensive; the latter, where individuals speak for a wider constituency, is feasible through appropriate connections, for example access to community groups or related data. To achieve a balance of power, facilitation can be critical, helping with seemingly simple strategies like seating plans and titles, as well as ground rules and agenda setting.

Learn as we go

We need to study co-production as a topic, over and above the focus of the research in which it is used. Ideally, those involved in co-productive research will:

  1. draw on the plentiful, useful but largely dispersed literature that can provide evidence for what works where;
  2. use an existing framework and model; and,
  3. commit to the study of their initiative – for example testing the adopted framework – for the benefit of the field. Rather than more lists of barriers and facilitators, we need studies of co-production in action.

Conclusion

Of course, co-produced research will ultimately only be as successful as the broader system enables it to be. Funders and health care and academic organisations also have a role to play (see my blog post with Allan Best; Six actions to mobilise knowledge in complex systems).

What has your experience been with research co-production? Can you point to useful theories, models, frameworks and methods?

References:
Boivin, A., Lehoux, P., Burgers, J. and Grol, R. (2014). What are the key ingredients for effective public involvement in health care improvement and policy decisions? A randomized trial process evaluation. Milbank Quarterly, 92: 319–350. Online (DOI): 10.1111/1468-0009.12060

Holmes, B., Best, A., Davies., H, Hunter., D, Kelly, M., Marshall, M. and Rycroft-Malone, J. (2016). Mobilising knowledge in complex health systems: A call to action. Evidence and Policy, 12, 3. Online (DOI): 10.1332/174426416X14712553750311

This blog post is based on a longer version published on the Michael Smith Foundation for Health Research website.

Biography: Bev Holmes PhD is Interim President and Chief Executive Officer of the Michael Smith Foundation for Health Research, a research funding agency in British Columbia, Canada. She holds adjunct professor appointments at Simon Fraser University and the University of British Columbia. Her research interests include knowledge translation, discourse analysis, health communication, risk communication, and public involvement in health research.

Three schools of transformation thinking

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Community member post by Uwe Schneidewind and Karoline Augenstein

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Uwe Schneidewind (biography)

‘Transformation’ has become a buzzword in debates about sustainable development. But while the term has become very popular, it is often unclear what is meant exactly by ‘transformation’.

The fuzziness of the concept can be seen as a strength, giving it metaphoric power and facilitating inter- and transdisciplinary cooperation. However, this fuzziness means there is also a danger of the transformation debate being co-opted by powerful actors and used strategically to impede societal change towards more sustainable pathways.

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Karoline Augenstein (biography)

Thus, issues of power are at stake here and we argue that a better understanding of the underlying assumptions and theories of change shaping the transformation debate is needed. We delineate three schools of transformation thinking and their assumptions about what drives societal change, and summarize them in the first table below. We then examine the relationship of these three schools of thinking to power, summarized in the second table. Continue reading →

Creating community around the Science of Team Science

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Community member post by Stephen M. Fiore

Stephen M. Fiore (biography)

How can we create new academic communities? I provide lessons from building the Science of Team Science (SciTS), a rapidly growing cross-disciplinary field of study. SciTS works to build an evidence-base and to develop translational applications to maximize the efficiency and effectiveness of team-based research.

I particularly draw lessons from the recent 8th annual conference attended by approximately 200 people. The conference aimed to:

  • disseminate the current state of knowledge in the SciTS field along with applications for enhancing team science;
  • provide opportunities to discuss future directions for advancing SciTS to improve the global scientific enterprise; and,
  • provide opportunities for interaction amongst a diverse group of stakeholders, including thought leaders in the SciTS field, scientists engaged in team-based research, institutional leaders who promote collaborative research, policymakers, and federal agency representatives.

Continue reading →

What can action research and transdisciplinarity learn from each other?

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Community member post by Danilo R. Streck

danilo-streck
Danilo R. Streck (biography)

A man raises his hand and brings up the following issue: “Our community is constantly affected by terrible floods that not only destroy our houses, but are the cause of sicknesses of our children.” This statement—in the midst of a participatory budget meeting in South Brazil—raised issues concerning the deforestation of riverbanks, the deficient sewage system, contested land ownership and occupation, among others.

Our research group is primarily interested in citizenship education and in supporting it through studying what makes learning possible (pedagogical mediation) within discussions about the allocation of resources for the public budget. Stories like this one remind us of the limits of a simplistic approach to understanding citizenship. In this case, citizenship and citizenship education was clearly related to health, to ecology, to urban planning, to farming, among other fields of acting and knowing.

Action research, broadly understood as collective (self) reflection in action within situations that one wants to change, is intrinsically an exercise of disciplinary transgressions. Continue reading →

Synthesis centers as critical research infrastructure

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Community member post by Andrew Campbell

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Andrew Campbell (biography)

When we think of research infrastructure, it is easy to associate astronomers with telescopes, oceanographers with research vessels and physicists with particle accelerators.

But what sort of research infrastructure (if any) do we need in order to do more effective multidisciplinary, interdisciplinary and transdisciplinary research on big, complex, ‘wicked’ challenges like climate change or food security?

Some eminent colleagues and I argue in a new paper (Baron et al., 2017) that the answers include: Continue reading →