Social mechanisms

One of the key tenets of realism is the very basic idea that observational evidence alone cannot establish causal uniformities between variables. Rather, it is necessary to explain why the relationships come about; it is necessary to establish what goes on in the system that connects its various inputs and outputs. In this manner, physicists are able fully to understand the relationship between the properties of a gas (as measured by the variables—pressure, temperature and volume) using knowledge about the kinetic action of the constituent molecules. In pharmacology, the term ‘mechanism of action’ refers to the specific biochemical interaction through which a drug substance acts on the body to generate its curative effect. Programme evaluators do not suppose that CCTV (the intervention) causes a fall in crime rates (the outcome). It does so, when it does so, by persuading potential perpetrators of increased risks of detection (the mechanism). In all cases, science delves into the ‘black box’. In all cases, the mechanism is what generates the observed relationship.

Whilst it is possible to recognise the affinities in explanatory structure across these examples, they also demonstrate that the action of the generative mechanisms is quite different, to such an extent indeed that that they defy a simple, unitary definition of their nature and content. Pawson expands on the applications of generative vs successive conceptualisations of causation elsewhere [2].

Readers of this journal will need no reminding that these paradigms have been debated for many years. Realists see physical and social reality as stratified and emergent. Things that cannot be cast as variables yet are vital to explanation (like kinetic forces, cultural norms and human interpretation or agency) are missing from correlational methods. Causal associations themselves are rarely universal; they are adaptive ‘demi-regularities’, which are always strongly influenced by setting and context. The original sources for these arguments may be found in Hesse [3], Harré [4], Pawson [2,5], Sayer [6,7], Bhaskar [8], Boudon [9] and Stinchcombe [10].

We acknowledge the further cleft between ‘critical realism’ and ‘scientific realism’. The writings of Bhaskar [8,11] and Pawson [2] serve as a reasonable proxy for these two schools. They differ on the matter of whether social science can create ‘closed system’ investigations. For Bhaskar, the closed system, experimental control available to the natural scientist is not achievable in social research because of ever-present emergence, that is to say the unique and unceasing human capacity to change the circumstances in which they live. As a ‘substitute’ for closed system empirical enquiry, he thus proposes the usage of abstract, a priori reasoning and the admission of a moral lens through which to critically evaluate human actions ([11], p. 64). Pawson, by contrast, argues much more pragmatically that neither physical science nor social science investigation depends on the achievement of closed systems ([5], p. 67). There are no crucial experiments (most especially Randomised Controlled Trials) which alone furnish us with social laws. But equally, natural science only ever makes slow and imperfect progress in gathering knowledge of the potentially infinite number of contingencies that can shape a physical system. Investigatory closure is always partial. Again, we are presented with rather different visions, the only contradiction occurring when an investigation claims to be both normative and scientific.

For Archer [12], collective, constrained decision making is the underlying mechanism that creates all social outcomes. Society is made by but never under the control of human intentions. At any given time, peoples’ choices are conditioned by pre-existing social structures and organisations. We are thus externally constrained in our actions but always part of human agency is the choice to attempt to change the initial conditions that bear down on us. These adaptive choices, over time, go on to mould novel structures and changed institutions. Collectively, our present decisions congregate to form new systems, which in their own turn, constrain and enable the choices of the next generation. Society is thus patterned and re-patterned by wilful action, but as Archer reminds us, the causal outcomes never conform to anyone’s wishes—even the most powerful.

Most realists would affirm this broad account of the mechanisms of social change, where structures shape actions, which shape structure, which shape actions, and so on. There are, however, some significant differences in where they locate the precise locus of that change. For Bhaskar [8], causal mechanisms sit primarily within the structural component of the social world. They reside in the power and resources that lie with the great institutional forms of society. For other realists, such as Pawson and Tilley [1], mechanisms are identified at the level of human reasoning. Thus, mechanisms can have different meanings depending on the scope of the intended explanation. Structural mechanisms come to the fore if the social scientist is attempting to explain large-scale social transformations. If, however, the researcher is attempting to discover whether a particular fitness programme creates healthier participants, it can be assumed that key outcomes will result from the reasoning and responses of the participants.

Mechanisms in evaluation

We survey this broader terrain as a prelude to focussing on the more specific version of mechanism thinking referred to by Pawson and Tilley that has come to play a key role in the evaluation of social interventions, namely realist evaluation [1], which is the main focus of this article.

Mechanisms in realist evaluation

Within the scientific realism approach, Pawson and Tilley [1] have provided their own conceptualisation of mechanisms; mechanisms are a combination of resources offered by the social programme under study and stakeholders’ reasoning in response [1]. They state that mechanisms will only activate in the right conditions, providing a context + mechanism = outcome formula as a guiding principle to realist enquiry [1]. This article sits within the empirical application of realism in the form of realist evaluation and the usage of mechanisms therein. In particular, we make a case for the explicit disaggregation of resources and reasoning in implementation endeavours, to which task we now turn.

The units of analysis within realist evaluation are programme theories—the ideas and assumptions underlying how, why and in what circumstances complex social interventions work. Many readers will by now be very familiar with programme theories expressed as CMOc and with the fact that data collection and analysis in realist evaluation centres on the process of developing, testing and refining CMOc. In the next section of the paper, we propose a development of this formula, which aims to facilitate the study of implementation processes and interventions.

1 The concern

Realists posit that exposing not only the mechanisms of change in an intervention but more importantly their relationship to the context of their implementation is key to the evaluation of complex programmes [20,26]. However, deciding whether aspects within an intervention implementation process in a realist project contribute contextually or mechanistically to the overall explanatory endeavour has become the realist researcher’s quandary [14,23,27]. Like these authors, we encountered challenges in distinguishing between context and mechanism in our evaluation of the ICP and were cognisant of the need not to conflate programme strategy (the intervention) with mechanism. We concur with Jagosh et al. [23], who note how it is not always as straightforward as might be assumed to map the complexities of the transformation process and the multiple systems within which it operates onto the C + M = O formula. Arguably, outcomes can be identified with most ease; they are observed or measured or at least aimed at with a degree of clarity. Although the distinction between resources and reasoning is used in Pawson and Tilley’s seminal work [1], their relative importance in understanding mechanisms is often understated. Consequently, researchers often emphasise one at the expense of the other, under the banner of mechanism [25]. To address this, we offer the solution below.

2 Our way forward

Building on the original work of Pawson and Tilley [1], we would like to propose an alternative operationalisation of the CMOc formula:

Intervention resources are introduced in a context, in a way that enhances a change in reasoning. This alters the behaviour of participants, which leads to outcomes.

Resources and reasoning are mutually constitutive of a mechanism, but explicitly disaggregating them can help operationalise the difference between a mechanism and a context. Although resource and reasoning are made explicit in the seminal work of Pawson and Tilley [1], they have often not been referred to explicitly in subsequent research. In our own study, through using this formula, it became clearer whether data contributed contextually or mechanistically, as we could identify mechanism components (resource and reasoning) which are different to contexts. Figure 1 illustrates how we have presented the new formula diagrammatically in the ICP study. Through trial and error, it became clear that the original formula could be built upon, hence the new formula which disaggregates resource and reasoning, placing ‘context’ in between. However, this is not to be confused with just using resources without reasoning—they must always come as a pair. It is important to note here that this new formula is only an extension of the original heuristic developed by Pawson and Tilley [1]. This new formula does not aim to re-draw the full sequence of causation but to modify the basic heuristic to aid operationalisation of realist approaches.

Differentiating between resource (the component introduced in a context) and reasoning therefore helps distinguish between relevant context and mechanism. Identifying the resource is contingent on the purpose of the study, and identifying the reasoning avoids the issue of conflating programme strategy (resource) with mechanism.

3 The social science illustration

In the palliative care ICP study, an outcome pattern was observed that practices identified and placed fewer palliative patients with non-cancer illnesses on their palliative care registers, in comparison to those with cancer illnesses. This was common across all 14 practices studied and was particularly noticeable for patients residing in care homes, where many older adults have non-cancer illnesses. Patients with non-cancer illnesses have unpredictable illness trajectories, meaning that registering this patient group is challenging for health care professionals, as a period of significant decline can be followed by substantial improvement, despite a downward trend in wellness [28,29]. Comparatively, this is not the case with cancer diagnoses as often there is a specific diagnosis and steady illness trajectory. We aimed to generate a CMOc to explain why there were less palliative care registrations of patients with non-cancer illnesses than cancer patients (outcome). In attempting to formulate the configuration, we were uncertain whether the context was the unpredictable illness trajectories of older adults without a cancer diagnosis, or care homes in general or the palliative care register being difficult to use with non-cancer patients. Breaking down the C + M = O formula to include resource and reasoning using the new formula, M (resource) + C → M (reasoning) = O, helped in deciphering the context from the mechanism. The use of the new formula diagram (Figure 2) also helped in configuring the whole CMOc. Figure 2 displays the novel way in which the new formula should be represented diagrammatically. Through using the new formula and associated diagram, it became clear that the resource was the palliative care register which, when used with older adults who had unpredictable illness trajectories (context), resulted in anxiety in registering these patients (reasoning), which meant that less older patients in care homes were registered (outcome) (Figure 2). Through understanding that resources were introduced into pre-existing contexts in a way that altered the participants’ reasoning, it becomes easier to explain the differential registration numbers (outcome).

Disaggregating resources and reasoning encourages researchers to consider both concepts, rather than privileging one at the expense of the other and will contribute significantly to the explanatory endeavour of the realist researcher. It is important to understand the new formula (M (Resource) + C → M (Reasoning) = O) highlights that resources must be introduced into a pre-existing context, which in collaboration induces an individual’s reasoning, leading to an outcome. Distinguishing the resources that are introduced into contexts from the reasoning this generates can provide both an operational and a conceptual clarification of mechanism. It can enable researchers to clearly understand the role of context in triggering mechanisms, thus developing their explanation of how interventions work. We now turn to interrogate the notion of mechanisms being ‘triggered’ in the next section of the paper.

1 The concern

A separate but related difficulty encountered when using mechanisms in social science research is the notion that mechanisms are often said to ‘fire’, ‘trigger’ or ‘modify’ in context to create an outcome [1,30-32]. Pawson and Tilley [1] use the much referenced gun powder analogy to explain this. When a spark is introduced to gun powder, the chemical composition of gun powder (mechanism) results in an explosion (outcome). However, there are no explosions if the context is not right—damp conditions, insufficient powder, not adequately compact, no oxygen present, duration of heat applied is too short (context). Thus it purports that causal outcomes follow from mechanisms acting in contexts; this is the base from which all realist explanation builds. Most complex social interventions involve stakeholders’ volition (reasoning). As Pawson [33] states, “much more than in any other type of social programme, interpersonal relationships between stakeholders embody the intervention” [33]. We found it difficult to apply the firing analogy to interventions where human volition is entwined in the intervention. Reasoning in these cases is rarely activated via an on/off switch, triggered in favourable contextual conditions. Instead, activation operates along a continuum similar to the light created by a ‘dimmer switch’, where intensity varies in line with an ever evolving context. Our experience suggests that researchers are often enabled to develop their realist thinking further when this myth of on/off reasoning is dispelled. The metaphor of the dimmer switch accommodates the activation of new volition as well as the idea of continuums of activation.

2 Our way forward

Conceptualising volition as happening in a binary ‘firing’/‘not firing’ fashion masks a continuum of activation which can have more explanatory value in understanding how interventions work. There are varying degrees to which an individual can feel confident, angry or mistrustful, leading in turn to a gradation of outcomes.

3 The social science illustration

In our evaluation of the ICP, we observed that the volition of health care professionals was always on a continuum. Health care professionals felt anxious when registering older adults with an illness other than cancer, as the trajectory of such illnesses is so unpredictable (Figure 2). Health care professionals could not predict patients’ decline, did not wish to over populate their palliative care registers and were worried about registering patients who seemed relatively well but could decline quickly. Furthermore, once a decline in health begins in older adults with non-cancer illnesses, it can be very rapid and thus end-of-life care is implemented quickly and is often unplanned, which can result in a death that does not adhere to patient preferences. The anxiety of health care professionals working with palliative non-cancer patients was evident, yet this anxiety did not switch on and off, it developed over time, as patients’ illnesses progressed. It also differed between health care professionals; those with more experience of working with patients with non-cancer disease had less anxiety about registering them. Thus the reasoning of having anxiety was on a continuum for health care professionals using the palliative care register. There is a variation in the amount of anxiety a health care professional will feel when registering a patient with a non-cancer illness, it is not dichotomised; the degree to which this is felt is combined with a facilitative context and appropriate resource. This should lead to a more appropriate use of the palliative care register.