3.1 Personalisation in social care policy: Scotland and beyond

Social and health-care services are in a time of radical change around the world (King’s Fund, 2014), in particular due to demographic changes resulting in greater service demands (Office of National Statistics, 2015). Models of social care service delivery are also evolving, with the integration of social and healthcare (Scottish Government, 2018; Social Care Institute for Excellence, 2017), a focus on prevention (Government Office for Science, 2015), and partnership approaches to support that are based on the concept of co-production (Scottish Government (‘Christie Commission’), 2011).

One such approach is personalisation (Needham, Reference Needham2011; Needham and Glasby, Reference Needham and Glasby2014; Needham, Reference Needham and Bevir2016; Leadbeater, Reference Leadbeater2004). While Needham characterises personalisation as a ‘way of thinking about services and those who use them, rather than being a worked out set of policy prescriptions’ (Reference Needham2011: 55), the common thread of personalisation policies is that individual needs and preferences are placed at the heart of services. This opens up a wide choice of policy options, the most widely known being personal budgets (Needham, Reference Needham2011).

Personalisation is undisputedly part of the marketisation of social care (Holloway, Reference Holloway2007; Pearson and Ridley, Reference Pearson and Ridley2017), but it nonetheless provides the opportunity for the co-productive involvement of service users in the design, commissioning and delivery of the social care services they receive (e.g. Bracci and Llewellyn, Reference Bracci and Llewellyn2012). This includes greater choice around the content and provision of services, which is often addressed through the allocation of personal budgets to individuals who meet specific eligibility criteria (Duffy, Reference Duffy2007; Le Grand, Reference Le Grand2007). It can, however, also extend beyond the allocation of budgets, including an individualised and flexible schedule of social care (Duffy, Reference Duffy2007).

Personalisation can be a double-edged sword (Ferguson, Reference Ferguson2007; Needham, Reference Needham and Bevir2016) that can empower service users (Needham and Glasby, Reference Needham and Glasby2014) but also increase user risks (Ferguson, Reference Ferguson2007; Needham and Glasby, Reference Needham and Glasby2014; Ismail et al., Reference Ismail, Hussein, Stevens, Woolham, Manthorpe, Aspinal, Baxter and Samsi2017; Pearson and Ridley, Reference Pearson and Ridley2017). This is the result of competing motivations for introducing personalisation policies, one based on the ideals of the independent living movement in disability activism (Oliver and Sapey, Reference Oliver and Sapey2006), and the other propelled by austerity-driven savings (Power, Reference Power2014; Pearson and Ridley, Reference Pearson and Ridley2017). To add further complexity to personalisation as a policy, individual implementation factors, such as the role of service delivery staff (Mason et al., Reference Mason, Spinks, Hajkowicz and Hobman2014), have been found to influence overall outcomes and create variation in the service-user experience.

In the Scottish context, there are two notable policies that follow this personalisation approach: Self-Directed Support (SDS), which aims to enhance the autonomy of service users regarding their care (Audit Scotland, 2017: 11; Social Care (Self-Directed Support) (Scotland Act, 2013), and the Reshaping Care for Older People (RCOP) initiative (Scottish Government, 2012), which seeks to empower older people and their carers and actively involve them in decisions about their own care provision. RCOP was supported by the £230 million Change Fund for Older People (Scottish Government, 2012), whereas SDS benefited from additional central government funding for local councils (Audit Scotland, 2014).

While Scotland was slow to incorporate market elements into social care (Pearson and Ridley, Reference Pearson and Ridley2017), SDS in particular was one of the Scottish government’s flagship policies that heralded a new approach to social care.

Similar to personalised care options in England and Wales, SDS provides service users with four options to fund and commission their own care package, ranging from full local authority provision of services to direct payments (Social Care (Self-Directed Support) (Scotland Act, 2013) (see Figure 2).

Audit Scotland highlights in its latest audit of SDS that it is ‘based on the human rights principles of fairness, respect, equality, dignity and autonomy for all. This means that people should be equal partners with relevant professionals in determining their social care needs and controlling how their needs are met’ (Audit Scotland, 2014: 5).

Of particular interest for a co-production analysis is Section 1(3) of the Social Care (Self-Directed Support) (Scotland) Act 2013, which states that service users should be enabled ‘(a) to express any views the person may have about the options for self-directed support, and (b) to make an informed choice when choosing an option for self-directed support’. The Act thus seems to promote user-driven decision-making that should entail co-design and co-innovation (see Figure 1).

Yet, there is great variation in how successful RCOP and SDS have proved in practice (Audit Scotland, 2016, 2017). Pearson et al. (Reference Pearson, Watson and Manji2017) point out the strategic role of co-production in the context of SDS and argue that it partly accounts for variations in service-user experience. However, their study does not differentiate between different forms of co-production and thus does not focus on the underlying challenges for different co-production categories. This is one of the current research gaps that our paper seeks to address theoretically and empirically.

We thus investigate the role of co-production and its management in personalisation policies, using the examples of RCOP and SDS, and identify related contingencies. Beyond being relevant for social policy design and implementation, our data are of particular interest for two reasons. First, Scotland’s approach to personalisation follows similar patterns to earlier approaches in England and Wales, meaning that findings from Scotland will be relevant across the UK and beyond. Second, the service experience of older people that we capture not only represents the core adult service-user experience but also allows us to capture the experiences of vulnerable service users, in our case people with dementia, whose capacity may make it difficult for them to actively participate in standard service interactions or who require support through carers (Brown, Reference Brown2010; Lymbery and Postle, Reference Lymbery and Postle2010; Jepson et al., Reference Jepson, Laybourne, Williams, Cyhlarova, Williamson and Robotham2016). Incapacity is here defined by the Adults with Incapacity (Scotland) Act 2000 as the inability to make, communicate, understand or retain memory of decisions without additional assistance.

Our theoretical contribution is twofold: first, we test Osborne et al.’s (Reference Osborne, Radnor and Strokosch2016) model empirically and add to the evidence base on co-production outcomes, which a recent systematic review has identified as an area for further research (Voorberg et al., Reference Voorberg, Bekkers and Tummers2015). Second, we identify three dimensions of co-production and link them to the co-production matrix, creating a framework to guide policy-makers in matching policy focus with co-production type.

4.1 Research objective and questions

This paper explores the role of co-production in personalisation policies in adult social care, addressing a research gap of relevance for co-production theory and our understanding of the implementation of personalisation in social policy reform. This leads us to the following four research questions:

1. RQ1: What forms of co-production, following Osborne et al. (Reference Osborne, Radnor and Strokosch2016), have been practised in the context of RCOP and SDS?

2. RQ2: What are the drivers and barriers to co-production in personalisation?

3. RQ3: How does the type of co-production relate to outcomes?

4. RQ4: How can current co-production theory inform social policy and legislative reform on personalisation?

4.3 Two case studies

The cases were located across two Scottish local authorities, spanning rural and suburban areas, capturing representative experiences applicable across Scotland and the wider UK. Case 1 focused on co-production as part of the Scottish Government’s 2011 initiative, Reshaping Care for Older People (RCOP). It benefitted from the Change Fund for the purpose of creating more community-based opportunities for older people, including vulnerable service users suffering from learning disabilities and dementia (Convention of Scottish Local Authorities, National Health Service and Scottish Government, 2013). The project was coordinated by the local Third Sector Interface (TSI) and had a remit to ensure that service users were involved throughout.

Case 2 was led by a local authority social care dementia unit, which was seeking closer collaboration with other social and healthcare actors across the public and third sectors to focus more on outcomes and service-user voices. Whilst it already had an expressed orientation towards co-production, the unit required significant further investment to embed these connections and a service-user focus, which was again supplied through the Change Fund. Further information on the two case studies is presented in Table 1.

TABLE 1.

Case Study Co-Production Projects and Types, Following Osborne et al. (Reference Osborne, Radnor and Strokosch2016)

A limitation to both case studies was the limited direct involvement of service users; this resulted from difficulties in standardising interviews across vulnerable service users and restrictions on their involvement on ethics grounds. We have hence included carers in our data collection; while certainly not identical with service-user views, carers proved important putting the service provider emphasis of our data into context.

4.4 Data analysis

The interview data are visually displayed in Figure 3, following Gioia et al.’s (Reference Gioia, Corley and Hamilton2012) data structure model, and are discussed in the following sub-sections.

Figure 3:

Gioia methodology coding process according to Corley and Gioia (Reference Corley and Gioia2004) and Gioia et al. (Reference Gioia, Corley and Hamilton2012)

5.1 RQ1: What forms of co-production, following Osborne et al. (Reference Osborne, Radnor and Strokosch2016), have been practised in the context of RCOP and SDS?

The interview data, supported by internal documentation, indicate a discrepancy between the intended co-production approach and its implementation. This is apparent throughout all three dimensions, but particularly the co-production potential dimension (see Table 2). Most actors felt that they ‘were already doing co-production before we knew it was co-production’ since ‘it just made sense to us’ (manager, advocacy group). Among frontline social work staff, however, there was some enduring scepticism. Some considered co-production to be ‘a flavour of the day’ (social worker in dementia care) – popular with the Scottish government, but not fundamentally different from what they had been doing before (corroborating the Osborne et al. (Reference Osborne, Radnor and Strokosch2016) conceptualisation of pure co-production as an integral part of the service experience):

Significantly, though, many social workers found it hard to differentiate co-production from consultation and often conflated the two. They argued that any service would have to take into account user input and feedback and that this had been part of their practice for many years. This would suggest that there was an awareness of ‘involuntary’ co-production as per the Osborne et al. (Reference Osborne, Radnor and Strokosch2016) model. Some failed to differentiate between ‘asking’ service users for their opinions (consultation) and users having direct control over the design and delivery of their services, in partnership with professional staff (co-design).

Those leading the co-production initiatives were aware of this particular tension and highlighted training and communication efforts to resolve the conflation between voluntary co-design and consultation/participation. In this case, however, there was little cognisance of unconscious co-production. A Third Sector Interface manager (case study 1) recalled:

Gaining views from service users on their understanding of co-production proved difficult and our data is insufficient to provide firm conclusions. An emerging theme, however, was a perception that co-production meant providing input rather than involvement in their services. Asked whether they felt they could shape their own services, a service user commented that ‘I don’t think that kind of service is available’. Future research would benefit from a specific focus on the understanding of co-production among service users and how it interacts with professional perceptions.

The majority of what was described as collective co-production entailed substantial elements of advocacy through the Third Sector on behalf of service users. Thus, Third Sector organisations collected user views and argued for the introduction of specific services, such as tea dances or singing groups. This seems to confirm findings from Bovaird et al. (Reference Bovaird, Van Ryzin, Loeffler and Parrado2015) regarding the challenges of citizen-led collective co-production. Lindsay et al. (Reference Lindsay, Osborne and Bond2013) suggest that metrics may have a role to play here, as measurement tends to capture structural interactions between organisations more easily than individual or collective outcomes.

Co-production and co-construction figured in both case studies by virtue of being intrinsically linked to the use of any service (Osborne et al., Reference Osborne, Radnor and Strokosch2016). They entailed, for instance, the attendance at dementia cafes, tea dances or movie viewings, which were initiated and organised by either statutory or Third Sector organisations. Service users were co-producing through their service experience (i.e. attending these events) but also their interactions with public service organisations (e.g. responding to questionnaires, attending co-production workshops). Case study 1 showed evidence of co-design, particularly through the locality structure that was part of its governance approach; service users were supported to actively contribute to council meetings and more local committees in order to shape their services. We did not find evidence of co-design in the context of case study 2. Co-innovation in both case study sites was guided by Third Sector and social work advocacy that structured collective involvement in service systems, e.g. by relaying service users’ views in Community Planning Partnerships or using their insights into the service-user experience to create new services.

TABLE 2.

Types of Co-Production, Following Osborne et al. (Reference Osborne, Radnor and Strokosch2016)

5.2 RQ2: What are the drivers and barriers to co-production in personalisation?

The qualitative data under each first-order concept were revisited to identify drivers and barriers of co-production, with some concepts fulfilling both roles depending on the context (e.g. resources), while others were not discussed as either (e.g. replication of co-production). We also matched the types of co-production described or implicitly inferred through the interview data to the corresponding aggregate dimensions. This allowed us to present drivers and barriers not just in relation to our three dimensions of co-production but also in relation to the co-production typology. Results are summarised in Table 3.

TABLE 3.

Drivers and Barriers to Co-Production, Following Osborne et al. (Reference Osborne, Radnor and Strokosch2016). Drivers and Barriers Discussed in more Detail Below are Highlighted in Bold

The results of the analysis support the previous finding that the Third Sector acted as a key driver of co-production, in particular using its advocacy role to co-innovate service redesign or new services (e.g. better local transport systems). The co-production potential dimension was associated most closely with co-design and co-innovation, involving voluntary acts of co-production beyond the service users’ experience (e.g. offering a new service based on one of their interests, such as co-hosting a dementia radio show). However, this is also the dimension in which most barriers were identified, such as a lack of time to get involved or a lack of support to make one’s voice heard.

Interviewees associated the majority of drivers with the dimensions of co-production implementation and environment, which related mostly to collective forms of co-production. For instance, additional funding drove new services created through Third Sector advocacy (co-innovation) and improved the collective service-user experience across the service system (co-construction).

We present a more detailed discussion of those drivers and barriers that are of particular interest due to one or more of three indicators: the number of sources mentioning the concept as a driver and/or barrier, the number of individual references across both case study sites, and new insights from the academic literature.

RQ3: How does the type of co-production relate to outcomes?

TABLE 4.

Findings on Outcomes and Co-Production Types for each Case Study, Building on Outcome Classifications from Bovaird and Loeffler (Reference Bovaird, Loeffler, Brandsen and Holzer2010). Outcomes were Evaluated and Reported by Case Study Sites and have not been Verified Independently.

The main issue in linking co-production types to outcomes was the actual measurement and reporting of outcomes (see Table 4). Interpreting outcomes proves difficult for two reasons: In case study site 1, contacts/referrals were used as a main indicator, with qualitative data presenting a positive but also highly localised view that makes it hard to evaluate the overall legacy of the co-production initiatives. Case study site 2 used measurements at a service level that indicated reductions in acute service expenditure, but these cannot be traced to any individual policy or project and may have other underlying drivers. Moreover, they say little about actual personal outcomes.

This evaluation is echoed by Audit Scotland, who found little systemic change in their overall audit of Reshaping Care for Older People (Audit Scotland, 2016) and highly variable outcomes in their report on Self-Directed Support (Audit Scotland, 2017). A key factor was the lack of a unified impact-reporting framework (Audit Scotland, 2016). While individual qualitative data supports anecdotal evidence that meaningful positive outcomes have been achieved, there is not enough robust data to confirm this conclusion.

Finally, our study followed an abductive approach to data analysis, with three dimensions of co-production emerging from the data: co-production implementation, co-production context and co-production potential. In analysing the first-order concept and second-order themes, we found voluntary types of co-production (co-design and co-innovation) to be associated above all with the co-production potential dimension. Thus, co-design and co-innovation emerged from statements relating to the service-user experience and to outcomes, which seems an obvious connection. However, they were also associated with key barriers to co-production. Conversely, co-production implementation and context were predominantly associated with co-construction and the drivers identified through the research. We suggest that this differentiation is emblematic of the divide between the policy intentions and potential of co-productive approaches to personalisation and the implementation-driven reality. This ties in with observations about the variable outcomes across Scotland (Audit Scotland, 2016, 2017) and previous observations about the role of co-production (Pearson et al., Reference Pearson, Watson and Manji2017).

In terms of implications for practice, we now introduce three propositions, based on the preceding discussion and our research findings, to address research question 4.

RQ4: How can current co-production theory and practice inform social policy and legislative reform on personalisation?

This matters because forms of engagement will differ across types and loci. For instance, we found that pure co-production was embedded in the service ethos of frontline staff, which could be reinforced if senior management focused more on involuntary co-production as part of the service experience rather than just voluntary ‘add-ons’. Conversely, the creation of facilitative forums, such as experience and advocacy groups including users (and carers) and guided by the Third Sector, may facilitate co-innovation at the service system level, even though it comes with its own risks. Specific interventions are needed for different forms of co-production; thus, it is important that there is conceptual clarity as to what form of co-production is to be encouraged, and alignment among the views of service users, frontline staff, management and policy.

We echo Lindsay et al. (Reference Lindsay, Osborne and Bond2013) and the Audit Scotland (2016) conclusions on RCOP, which show that data itself does not automatically lead to evidence-based decision-making. A meaningful system of comparable metrics needs to support organisations in their frontline activities rather than act as an additional administrative burden. Our data suggest that part of this evaluation strategy needs to be a more realistic timeframe that allows for outcomes to be tracked over time. The current focus on quantitative, population-level data does not seem fit for this purpose, neither does it prove insightful in understanding potential differences between service-user outcomes and service-provider priorities. While qualitative forms of impact recording offer more powerful insight into actual outcomes, they are also difficult to evaluate at policy level. This paper sadly cannot offer a panacea for this problem; however, it indicates that efforts have to be system-wide rather than only driven by individual organisations on a local level.

Current co-production theory highlights that co-production is far from dyadic: it requires an approach that includes a wider variety of stakeholders. This is particularly relevant in negotiating self-directed decision-making on services by vulnerable users, potentially involving risks to themselves, service staff and the wider community. Instead of risk management that seeks to minimise all such risks irrespective of the expected benefit, co-production theory strongly supports a negotiated risk discourse that includes service users, professionals, carers and the wider public. This may also help to prevent the ‘blame game’ effect (Hood, Reference Hood2002; Flemig et al., Reference Flemig, Osborne and Kinder2016) that a media predominantly focused on failed social policy innovation can trigger. This governance structure needs to be taken into account in policy design, not just at the point of implementation.