\\n\\n
Released this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\\n\\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'IntechOpen is proud to announce that 179 of our authors have made the Clarivate™ Highly Cited Researchers List for 2020, ranking them among the top 1% most-cited.
\n\nThroughout the years, the list has named a total of 252 IntechOpen authors as Highly Cited. Of those researchers, 69 have been featured on the list multiple times.
\n\n\n\nReleased this past November, the list is based on data collected from the Web of Science and highlights some of the world’s most influential scientific minds by naming the researchers whose publications over the previous decade have included a high number of Highly Cited Papers placing them among the top 1% most-cited.
\n\nWe wish to congratulate all of the researchers named and especially our authors on this amazing accomplishment! We are happy and proud to share in their success!
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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"4816",title:"Face Recognition",subtitle:null,isOpenForSubmission:!1,hash:"146063b5359146b7718ea86bad47c8eb",slug:"face_recognition",bookSignature:"Kresimir Delac and Mislav Grgic",coverURL:"https://cdn.intechopen.com/books/images_new/4816.jpg",editedByType:"Edited by",editors:[{id:"528",title:"Dr.",name:"Kresimir",surname:"Delac",slug:"kresimir-delac",fullName:"Kresimir Delac"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"3621",title:"Silver Nanoparticles",subtitle:null,isOpenForSubmission:!1,hash:null,slug:"silver-nanoparticles",bookSignature:"David Pozo Perez",coverURL:"https://cdn.intechopen.com/books/images_new/3621.jpg",editedByType:"Edited by",editors:[{id:"6667",title:"Dr.",name:"David",surname:"Pozo",slug:"david-pozo",fullName:"David Pozo"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"64879",title:"Numerical Simulation of the Effects of Increasing Urban Albedo on Air Temperatures and Quality over Madrid City (Spain) by Coupled WRF/CMAQ Atmospheric Chemistry Model",doi:"10.5772/intechopen.80473",slug:"numerical-simulation-of-the-effects-of-increasing-urban-albedo-on-air-temperatures-and-quality-over-",body:'\nAdaptation to climate change by increasing the reflectance of human settlements has been proposed as a simple and cost-effective geo-engineering strategy to offset the rise of temperatures associated with global warming at local and regional scales [1]. The use of higher albedo roofs and/or pavements (cool roofs and pavements) has shown effective surface air cooling in simulation experiments over many cities in the world [2]. Due to lower surface temperatures, an improvement in air quality can be obtained by slowing temperature-dependent photochemical reaction rates of formation of secondary pollutants, such as ozone, and reducing biogenic hydrocarbon emissions. Additional indirect benefits are linked to lower energy demand for summer cooling of buildings and its associated emissions in power plants [3]. On the contrary, due to the depression of the planetary boundary layer level (PBL) height caused by cooler temperatures, and to possible changes in local wind patterns, reduced mixing and dilution of pollutants can raise their levels by accumulation in some areas. Ground-level ozone (O3), particulate matter (PM), nitrogen oxides (NOx), and sulfur oxides (SO2) are most health-concerned pollutants, and their urban concentration levels can be affected by surface modification. O3 is a secondary pollutant resulting from the reaction between NOx oxides and volatile organic compounds (VOCs) in the presence of sunlight. Higher O3 concentration levels are directly related to warming in urban heat islands, reaching peak levels in summertime [4].
\nMost numerical simulations of the impact of albedo increase on pollutants have been developed over US cities, in general with different urban fabrics than in Europe, where compact mid-rise urban categories occupy most of the city centers. In pioneering mesoscale numerical simulations [5], extreme surface albedo enhancement resulted in ozone reductions in California. Simulating a more feasible albedo increase over southern US cities with high insolation levels, [6] obtained ozone reductions linked to cooler summer ambient air temperatures. However, only Sacramento showed significant peak ozone-level reductions due to a wider urban surface (25,000 ha). Applying comparable albedo increase levels, it has been simulated significant air quality benefits over California [7], suggesting that there is a maximum albedo increase implementation threshold above which no further benefits are obtained that should be determined for every urban case. There are scarce mesoscale simulation experiments to investigate the impact of albedo enhancement on pollutants other than ozone. In another simulation the effect on air quality during a heat wave episode of albedo increase at high latitudes (Montreal, Canada) was reported [8], with no significant effect on ozone levels, and slight reductions in PM2.5 levels (2 ppb), associated with a decrease in PBL height, that counteracted the cooling impact on ozone formation. Applying an extreme albedo increase over Stuttgart (Germany), a peak urban temperature cooling down to −1.7°C and decreases in mean ozone concentration were reported [9]. However, secondary undesirable effects were an increase of primary pollutants (NOx and CO) and an increase in peak ozone concentration due to a higher intensity of reflected UV shortwave radiation. To date, the impact of urban albedo enhancement on temperatures and air quality has never been assessed over Spanish urban areas by numerical modeling. Spanish cities may give key information for this research, due to the high levels of annual and summer insolation and to the hot summer Mediterranean climate in most of the country, with high annual number of clear skies that maximize the thermal and energy-saving potential benefits of changes in solar reflectivity [10]. On the other hand, an undetermined minimum critical intervention surface is also needed to obtain significant modifications in local atmospheric variables by land cover changes [6]. Madrid city is the biggest urban area in Spain, with broadly five million inhabitants in its metropolitan area, and the fourth most populated city in Europe. Emissions of air pollutants in Madrid are mostly originated from anthropogenic sources, with the traffic sector as the main contribution activity to the emissions of the whole region. In the last years, the Regional Government of Madrid has developed an ambitious action plan to improve the air quality for the period 2013–2020, called Plan Azul+. A WRF mesoscale simulation [11] showed that the Plan Azul+ measurements were effective in the reduction of NO2 levels over urban areas with high traffic influence. However, this simulation showed slight increases in ozone concentration (1–2%) in areas where typically ozone levels were low, and mitigation measures did not cause remarkable reductions in the rest of pollutants selected in the plan. Thus, prior to the establishment of recommendations for policymakers to include albedo enhancement in urban planning, the balance between potential climatic and air quality benefits and disturbances of widespread cooling the urban air must be assessed. Here, we have used a meteorological model (WRF), an emission model (AEMM), and a photochemical model (CMAQ) to assess the impact on meteorology and air quality of widespread urban albedo increase at two feasible levels of implementation: cool roofs (Alb1) and Alb2 (cool roofs + cool pavements). Changes in surface air temperatures and main pollutants are given for two 72-h period representative of summer and winter seasons.
\nNumerical simulations of urban surface modification over Madrid city have been designed to test the impact of two increasing surface albedo scenarios, conducted by coupling WRF/AEMM/CMAQ. Urban layer was simulated by an urban energy model (BEM) coupled with an urban canopy model for simulations [12]. We have considered up to 10 urban categories. Air quality analysis has been focused over the main pollutants of health concern, namely O3, NO2, SO2, CO, PM2.5, and PM10.
\nSurface modification was simulated over the urban land cover of Madrid city, which is located in the center of the Iberian Peninsula. Its geographical position and topography determine a temperate continental Mediterranean climate with cold humid winters, with temperatures usually below 0°C, and warm dry summer, with temperatures above 30°C, frequently reaching peak values over 40°C, and high nocturnal temperatures[13, 14].
\nIn Figure 1 we show nested modeling domains over the city of Madrid. Modeling is built over a mother domain (d01) with 27 km spatial resolution, centered at 40.383° N 3.717°W, and a domain size of 2727 × 2727 km2. This domain is intended to capture synoptic features and general circulation patterns. The first nested domain (d02), with a spatial resolution of 9 km, covers a domain size of 1575 × 1413 km2. The third domain (d03) with 3 km of spatial resolution has a domain size of 660 × 561 km2. The fourth domain covers the province of Madrid and nearest provinces, with an extension of 217 × 199 km2 and grid resolution of 1 km2. The innermost fifth domain encloses Madrid metropolitan area, covering 80.3 × 90.3 km2, and grid resolution 333 m.
\nUp: Modelling domains for simulations: d01, d02, d03, d04 and d05 (left), and d04 and d05 (right). Down: Madrid local municipality (red line) and surrounding metropolitan area [Images generated using Google Earth].
The air quality modeling system used to evaluate albedo scenarios was composed by a coupled WRF/AEMM/CMAQ model. To configure it, we have followed the guidelines indicated in the Guide on the use of models for the European Air Quality Directive [15]. Emission and photochemical modeling configuration used here have been previously validated elsewhere [11], using a numerical deterministic evaluation during the development of the Plan Azul+, considering the Maximum Relative Directive Error [15] referred in the European Directive EC/2008/50. Meteorological simulations have been performed using the Weather Research and Forecasting-Advanced Research WRF (WRF-ARW) version 3.2 [16], developed by the National Center of Atmospheric Research (NCAR). Urban categories have been adapted from The World Urban Database and Access Portal Tools [17, 18]. URBPARM.TBL was an adapted file for Madrid city according to our knowledge of urban morphology. Meteorology-Chemistry Interface Processor (MCIP) version 4.3 was used to prepare WRF output to the photochemical model. The annual anthropogenic emissions inventory of the Regional Government of Madrid has been used (version 2010). This inventory has a horizontal resolution of 1 × 1 km2 and includes emissions classified by Selected Nomenclature for Air Pollution (SNAP) sectors. We have used an Air Emission Model (AEMM) [11, 19] to adapt emissions to domains d04 and d05, using monthly and weekly profiles from the Unified EMEP model and vertical profiles from [20]. Emissions have been adapted under the requirements of the chemical modules considered in the photochemical model CMAQ. We have considered only anthropogenic emissions to avoid the influence of albedo modifications over the natural emissions, since the parameterizations that define them depend on meteorological conditions. This assumption is valid considering that the urban metropolitan area of Madrid is strongly dominated by anthropogenic local sources, being the natural emissions not very important, and only provide a remarkable contribution in areas far away of the city of Madrid [11]. To simulate the physical and chemical processes into the atmosphere, the US Environmental Protection Agency models-3/CMAQ model has been used [21]. Here, we have used CMAQ v5.0.1, considering CB-5 chemical mechanism and associated EBI solver [22] and AERO5 aerosol module [23]. Initial and boundary conditions for d04 domain are used from inner profiles and for d05 conditions are provided by the results of simulation of d04 domain. Coupled WRF/AEMM/CMAQ has been validated over Madrid city [11]. Another assessment of coupled WRF/CMAQ over Madrid city was reported [24], for an annual period and 1 km resolution, including a comparison of meteorological and air quality observations between WRF bulk urban canopy parameterization (UCP) (used here) and an alternative building energy model (BEP). As temperature predictions were not improved by WRF-BEP and the differences on wind direction and PBL height were not remarkable, we decided to use bulk UCP for computing time savings. Simulations have been executed over a computing cluster owned by Meteosim SL (Spain) and formed by 28 nodes and 308 cores.
\nMeteorological, emissions, and photochemical simulations have been conducted for two 72 h periods representative of both summer and winter of the year 2008: the period between June 30, 2008 and July 2, 2008 (hereinafter referred to as summer), and the period between January 1, 2008 and January 3, 2008 (hereinafter referred to as winter). The previous 24 h were taken as spin-up time to minimize the effects of initial conditions. A total amount of six simulations have been done for each period and three increasing albedo scenarios, defined from feasible levels of intervention obtained from literature [2, 25] as: (a) default scenario: using default value of albedo for all urban categories; (b) cool roofs scenario (Alb1): increasing from 0.20 to 0.55 the roof surface albedo for all urban categories; (c) cool roofs + pavement scenario (Alb2), with the same roof albedo increase as Alb1 plus an increase in pavement surface albedo from 0.15 to 0.30 for all urban categories. Spatial distributions and changes in pollutants levels at the innermost domain are reported, given as recommended time-weighted exposure parameters [26].
\nAveraged changes in surface albedo at the innermost domain resulting from the modification of roofs and pavements albedo over urban land cover are shown in Figure 2a and b. Maximum albedo change occurred as expected at the most compact area of town center, with +0.2 for both scenarios. No significant difference observed in this peak albedo increase between both intervention levels (data not shown for Alb2), was due to low ratio pavements/roofs at the dense urban structure of the urban category where they occur, tagged as compact mid-rise in the WUDAPT. On the contrary, surface albedo change differences were observed between scenarios at the open mid-rise category (enclosing most of the rest of the urban area inside the M40 highway belt), with +0.06 for Alb1 and +0.1 for Alb2, respectively. These two categories of residential use encompass the major proportion of urban potential intervention areas, along with the southeast belt of industrial-commercial use, where albedo increased +0.14 and +0.18, in Alb1 and Alb2, respectively.
\nSpatial distribution of average albedo (a, b), and midday (12 UTC) temperature differences (°C) between default and both Alb1 (c, d) and Alb2 (e, f) scenarios, and for the winter (a, c, e) and summer periods (b, d, f) at 12 UTC. Black lines limit municipalities, the biggest and innermost boundary is Madrid city (605.77 km²).
The spatial distribution of temperature changes was dependent on both the distribution and the level of albedo change. In Figure 2c–f, 72-h averaged changes at 12 h UTC are given. City center (compact mid-rise urban category) showed the most intense cooling in all cases, reaching the highest midday cooling intensity in summer of −1.4 and −1.6°C, for Alb1 and Alb2, respectively (Figure 2d and f). Little temperature change was observed in non-urbanized areas with small surface modification. In winter, much lower but significant cooling occurred, with maximum levels of −0.4 and −0.5°C, for Alb1 and Alb2, respectively, at the city center as well (Figure 2c and e). Thus, albedo enhancement was much more efficient in cooling air surface temperatures during summer periods, due to higher solar incidence angle. The spread of cooler air from city center toward the NE was due to predominant SW winds during the summer period, with averaging speed of 9.7 m s−1 (data not shown).
\nAfter albedo enhancement, changes in pollutants were characterized by a decrease in O3 in both periods, but higher in summer, and an increase in NO2 in both periods. Averaged values for every 72-h period and scenarios are given in Figure 3. Spatial distributions of changes for ozone are given in Figure 4, in both scenarios and at the innermost domain. For the rest of pollutants, Alb1 scenario changes are given in Figures 5 and 6. Slight increases in PMx and in SO2 occurred in winter with negligible changes in summer. Little changes were observed in CO levels.
\nMean change in pollutants concentration after albedo increase averaged for the winter (a) and the summer period (b).
Spatial distribution of changes in O3 maximum 8 h levels between default and Alb1 (a, b) and Alb2 scenarios (c, d) and for winter (a, c) and summer (b, d) (μg m−3).
Spatial distribution of changes in NO2 (a, b) and SO2 (c, d) maximum 1 h levels between default and Alb1 scenario for winter (a, c) and summer (b, d) (μg m−3).
Spatial distribution of changes in PM2.5 daily value (a, b) and CO 8-h maximum levels (c, d) between default and Alb1 scenarios for winter (a, c) and summer (b, d) scenarios (μg m−3).
When cool pavements were added to cool roofs (Alb2), differences in distribution of pollutants other than ozone were not remarkable (Alb2 changes in Figure 3). Areas of major changes in pollutants after albedo increase extended through city center and NE rural areas in summer. This NE spread during the summer period was again associated due to dominant SW winds in those days (data not shown). Highest reduction in O3 levels occurred during the summer period (Figure 4b and d), when more intense cooling occurred as well. Eight-hour maximum reductions of around −4 μg m−3 were reached at the city center. These results show that widespread cool roofs deployment over Madrid would benefit ozone levels at the city center, with additional reductions upwind depending on meteorological conditions. If additional cool pavements were implemented (Alb2), ozone reduction would extend further across most of the city, though reductions below the −4 μg m−3 threshold were not observed in this scenario. In winter period both scenarios show scarce benefits for ozone reduction, as expected from limited surface air temperature cooling and lower rates of ozone formation in default scenario (data not shown). Our ranges of O3 reduction are in accordance with similar mesoscale experiments of albedo modification [7, 9] with no local increases detected in our case, as other simulation studies have reported [3]. After albedo modification, NO2 levels increased, mainly at the city center and at Barajas airport in winter (Figure 5a and b) only Alb1 data are shown. Winter reductions were observed at some highly populated areas NW of Madrid city. At both periods, peak increases in 1 h—maximum concentrations reached up to +20 μg m−3. At the city center, summer increases were below winter changes, though peaks of +20 μg m−3 were reached at the pollutants spreading area NW of the city due to wind conditions. However, and contrary to O3 changes, spatially averaged changes in NO2 were very similar at both periods (Figure 5).
\nIncreases in SO2 occurred in winter at the highly populated municipalities SW of the city and around the airport and NE corridor, with peak 1 h maximum differences of 10 μg m−3 (Figure 5c and d). Decreased levels were observed in some municipalities west of the city. No significant changes occurred for summer in SO2 levels in Madrid city, with slight increases NE of the airport and SE of the municipality. Spatial changes between scenarios were not remarkable for the rest of pollutants (data not shown).
\nSlight differences were observed in the distribution of changes between PM2.5 (Figure 6a and b). Increases in PM2.5 were much higher in winter. Peak daily values increased above 2 μg m−3 in both scenarios, located around Barajas airport and south of the city. In summer on the contrary, little changes in PM2.5 were observed for both scenarios, with small increases in the city center below 0.4 μg m−3. For CO, small increases were detected in city center (Figure 6c and d), with maximum +0.1 mg m−3 also around the airport and SW of the city.
\nAccording to our simulations, it was clear that albedo enhancement caused citywide air cooling with associated pollutant changes directly linked to temperature reduction. Our model shows that cool roofs and pavements reduce outdoor temperatures slowing reaction rates of ozone formation [3]. However, observed increases in the levels of other pollutants were caused by depression of PBL height associated with cooler air, limiting pollutant dispersion, and vertical mixing. Spatial distribution matches with cooling and shows peak change levels below 100 m (Figure 7), in areas where pollutants show higher increments. In summer, PBL height can fall to 90 m, but lower emissions and meteorological conditions generated lower increments. O3 summer reductions by slower formation rates must probably be partly offset by PBL fall. In terms of averaged changes from maximum levels at default scenario inside the limits of Madrid city after implementing the highest level of surface modification (Alb2 scenario) were approximately summer reductions in O3 around to −4.4% and winter increments around +16% for NO2, +10% for SO2 and PM2.5, and +5% of CO. If only cool roofs were implemented (Alb1), these maximum thresholds would be the same for all pollutants but with a lower spatial reduction at the urban center for O3 (data not shown)
\nTemperature at 2m (a) and PBL height (b) differences between Alb2 scenario and default scenario for the winter period at 14 UTC.
Thus, our results confirm that citywide cool roofs deployment is a feasible effective measure to reduce summer air temperatures and control the ozone pollution over Madrid metropolitan area. Further benefits can be obtained extending albedo enhancement with cool pavements. In consequence, this surface modification strategy should be considered along with other actions in air quality plans over Madrid. According to numeric simulations of the application of measures proposed in Plan Azul+, carried out with a similar modeling approach [11], 2% O3 increases would be expected over Madrid region and up to +6 μg m−3 (1 h-maximum) at the center of town. For the rest of pollutants, the simulated effect of the Plan predicted reductions in NO2 up to 11 μg m−3 but only slight global reductions below 5% of CO, PM10, PM2.5, and SO2. However, our simulation shows undesired impacts of albedo enhancement that need further research before a real implementation was to be considered, as in winter cool roofs might partly offset the reductions predicted in the plan for pollutants other than ozone. Anyhow, and given the nonlinear dynamics of the atmospheric processes, it would be advisable to make new simulations for longer representative seasonal periods, combining measures of Plan Azul+ with increasing levels of intervention of surface albedo change, to determine the balance between benefits and disadvantages on air quality of a global action plan. A key outcome of our simulation is that the increment in pollutants other than ozone occurs mainly in winter period, where ozone formation rates are low and the concentration of other pollutants is higher. On the contrary, in our summer simulation, albedo caused limited increases in these pollutants, along with a maximized ozone reduction. According to our results, an ideal implementation of cool roofs and/or pavements would be a seasonally changing system of increased reflectance only on warm periods, with little or no albedo change for colder months. Furthermore, such a system would avoid winter penalty due to increased heating demand [27]. A real experience of seasonal surface albedo change is applied over 20,000 reflective greenhouses in Almeria province, 500 km south of Madrid, where whitewash slaked/lime painting is applied over the roofs to limit excess heating inside the greenhouses in summer and is washed out in September to allow enough winter radiation inside them. The implementation of high albedo in the area has caused mean outdoor surface air temperature cooling, locally offsetting the impact of global warming [28, 29]. The levels of albedo enhancement simulated here (round +0.1 at the pixel level) are similar to those implemented on the field over Almeria area, but with more than double intervention surface at Madrid urban area, well above the minimum critical intervention area for efficient cooling at similar comparable latitudes and insolation. As our temperature data show, expected changes in net solar income at the surface should be comparable in both observed and simulated experiences, with differences in air temperature impact due mostly to location and surface canopy parameters of the urban fabric. However, our results are site and time dependent and have been generated from the specific coupled modeling configuration applied over Madrid city, for the periods simulated, and for the emissions inventory used. Further extensive research with optimized mesoscale modeling should also include the impact of albedo enhancement on cloud cover and precipitation pattern, as rain causes wet deposition of pollutants improving air quality. As our results show, depression of PBL height and dynamics of cooler air over the city might cause reduced vertical mixing and affect to the dilution of pollutants [30]. Other undesirable effects on the microclimate of the city and surrounded areas cannot be discarded and should be studied, such as modifications in the wind pattern and the hydrological cycle in the region [31]. Finally, these results do not account for additional benefits such as reduced cooling energy use and associated reductions in emissions from point sources, neither on the potential negative impacts on heating energy use in winter [3].
\nThis study has been supported by the Spanish Government, Ministry of Economy and Competitiveness, Grant No. CGL2013-46873-R. The author is grateful to the Environmental Agency of the Regional Government of Madrid for providing emissions inventory.
\nThe author declares no competing financial interest.
In the UK, Third-Sector Organisations (TSOs) are a collective term for voluntary and community agencies, charities, and social enterprises, of which a sub-section provides health and social care via independent and value-driven services [1]. Recent audits of the whole sector reveal a notable presence, with over 160,000 organisations and nearly 1-million employees and volunteers operating in the UK [1]. Across many high-income countries, it is an area which is growing rapidly as governments seek to harness their innovation and local capabilities [1, 2]. Given their nature, TSOs tend to be highly regarded for their proximity to the community, welcoming facilities, and the ability to engage those with complex and chronic needs [1, 2, 3, 4].
Despite the potential benefits of TSOs, little research has been undertaken to evidence their impact and effectiveness [2, 3]. Research applicable to many mental health care TSOs in the UK, including systematic reviews [2], national audits [1] and interviews with mental health charities [3], highlight the clinical and economic barriers affecting the production and utilisation of practice-based evidence (PBE). Many are constrained by tight budgets and scarce resources and often exist as ‘micro-entities’ making bidding processes and research prohibitively expensive [1, 4]. The evidence that has been produced has been characterised as low in quality, lacking methodological rigour, theoretical modelling, and reliance on non-representative stakeholder feedback [2, 3]. Access to learning is equally challenging with constraints on resources to review the latest research literature [3, 4].
For TSOs to overcome these challenges, there must be greater alignment of needs and priorities between providers, commissioners, policymakers and academic institutions. One approach to optimising the production and sharing of knowledge has been to form collaborative learning networks (CLNs) of services using a similar treatment model or methodology for generating evidence [5]. By partnering with similar providers, these networks enable organisations to explore, share and integrate learning across a network, maximising the potential for practice-based learning. CLNs have demonstrable potential within the UK mental health care sector, having reported success in the Improving Access to Psychological Therapy (IAPT) programme [6] and Children and Young People’s [5] services. The IAPT programme, which is a national government-funded initiative for English primary mental health services, has been an influential driver in generating public domain service performance data. Having mandated sessional measurement across all services over a decade ago, it has recently achieved pre-and-post outcomes completion rates of 98% for clients completing therapy [7]. These high levels of data completeness are essential for supporting CLNs [6].
The quality implementation framework (QIF) [8] has been previously used as a schematic structure to introduce practice changes, including routine outcome monitoring (ROM), within mental health care services [9]. This model synthesises 25 implementation methods from almost 2000 evaluation reports, comprising 4 action phases and 14 critical steps [8]. Combined with research on the value of CLNs, an initiative was undertaken to bring together multiple TSOs delivering mental health care to enhance service quality. This chapter describes the rationale, process, and outcome of this initiative across its initial start-up and first year of operation using a traditional storytelling structure, with reference to the QIF [8] and other implementation frameworks [10, 11, 12, 13].
Implementation science is the scientific study of techniques to enhance the quality and effectiveness of health services by advancing the systematic uptake of evidence-based practice (EBP) in routine clinical settings [14]. The learning from the field demonstrates the gap between what is shown to be effective to what is implemented in practice [14]. According to the QIF, in preparation for implementing practice change, agents must assess the host setting and build capacity, meeting with the service, analysing its infrastructure, surveying and training practitioners, and securing buy-in [8, 9]. Regardless of how well-founded and robust the evidence may be, it is no guarantee it will be accepted and readily adopted by stakeholders [9, 15]. Persuasive communication is therefore critical for framing research findings for specific contexts to enhance their uptake and impact [16]. The power of storytelling is increasingly recognised as an effective technique for transforming attitudes, perceptions and behaviours as they summarise concepts simply, quickly and effectively, appealing directly to a stakeholder’s values and interest [16]. For instance, within UK mental health care services, storytelling as a technique has been associated with rapid improvements in data quality [9]. It is for this reason, our chapter aims to share the experiential learning and evaluation of this CLN for mental health care TSOs using a traditional storytelling outline, describing its setting, characters, plot, and themes.
To overcome the challenges of effective service development, a CLN was devised to support TSOs in the collection and use of data to inform the future development of operational practice. Inspired by the Institute for Healthcare Improvement’s (IHI) [12] ‘Breakthrough Series’ Collaborative Model and implementation science research [11, 12, 13, 14], this initiative intended to break new ground by working in close partnership with TSOs to generate evidence and inform quality improvement. The framework integrated implementation techniques using plan, do, study, act (PDSA) cycles [10] focusing on specific areas of service delivery and, as modelled by the QIF, create a structure for implementation [8, 9]. This would become known as the service improvement learning collaborative (SILC).
Working in partnership, TSOs were invited to upgrade their measurement system to a more sophisticated software platform providing additional reporting features relevant for service operation and development [17]. Services were required to verify their commitment and autonomy at a managerial, board and trustee level to commence on a year-long journey to profile and engage with subject-relevant resources and attend monthly mentorship sessions and quarterly overnight residentials. A memorandum of understanding was devised to emphasise that membership was contingent on full-service participation and this was incorporated into the development of an implementation plan [8, 9].
This project took place over the course of a year, focusing on a different challenge each quarter, including a focus on data collection, session attendance, endings, and clinical outcomes. The project commenced with a planning meeting involving introductions, training and attitudinal surveys. With reference to the QIF, these steps were undertaken to assess the fit between the organisation’s aspirations and readiness for change, allowing for open discussion and early feedback [8, 9]. Across the project, there were monthly supportive calls with an assigned mentor from the research team, and quarterly in-person residential meetings with fellow TSOs, each supported by in-depth data profiling throughout. The purpose of the mentorship and residential sessions were to support participants in monitoring aspects of service quality and provide supportive feedback mechanisms which, according to the QIF, are critical post-implementation support strategies [8]. To improve future applications, the end of the year culminated in a summative conference with fellow mental health services to share the findings from the project’s first year in operation [8, 9, 10]. A diagram of the SILC CLN model, including the induction, mentorship, residentials and summative conference, is outlined in Figure 1.
The SILC CLN model, adapted from the IHI [10] ‘breakthrough series’ collaborative model.
The QIF emphasises the criticality in creating an implementation team to oversee its rollout and set targets and agree off-track remedial action [8, 9]. The SILC project team was assembled in 2016, consisting of academics and clinicians with extensive experience in the field of talking therapies and service design [9]. This team was responsible for developing learning resources, providing mentorship support and tracking data through the relevant quarterly themes of service development. The team also worked directly with individual service leads to cascade learning and implement practice change, compiling routine reflective case notes and disseminating learning throughout the network.
A series of prospective pilot services were approached and recruited in early 2017, subject to expressions of interest and eligibility criteria. The SILC initiative was specifically aimed at mental health care TSOs using CORE IMS computerised quality evaluation systems [17] to obtain evidence on their delivery and strengthen their position for funding and benchmarking. Those eligible had been using CORE outcome measurement systems for over 5 years, primarily as an administrative tool to log clinical activity. Within all but one TSO expressing interest, there was little analysis of the data being undertaken, and no indication of it being used clinically or to enhance service quality. Prospective services were using traditional pre and post-therapy measurement approaches, acquiring outcomes data for around 40–50% of clients; a rate which is representative of the field and this methodology generally [18]. Many were also experiencing high rates of non-attendance and attrition, plus modest clinical outcomes for those with outcomes data.
The exploration phase of Aarons, Hurlburt and Horwitz [11] conceptual model for implementation identifies the importance of inner and outer contexts. In this project, it seems early withdrawal during the recruitment stages was due to a combination of socio-political factors and lack of absorptive capacity which impeded progress [11]. What had started as 12 prospective members soon halved to only six. Various reasons were given but discontinuation was mostly cited as being due to managerial turnover, lack of capacity for change, and workforce restructuring, or resistance. By contrast, the remaining TSOs demonstrated their levels of commitment via an initial attitudinal survey which, when disseminated to all practitioners (n = 49), achieved a high response rate of around 80%.
The six services joining the project ranged in size, geographical location and clinical specialism. Annual throughput ranged from around 80–300 clients per organisation. Clinical support specialisms included psychological support for female victims of domestic abuse; women on low incomes; parenting; unpaid carers; and general counselling support. Informed by QIF support strategies, each service was assigned a mentor from the SILC project team using a consultation and matching process [8, 9]. Members received regular updates via a monthly blog post on the project’s website (
Expanding on the story structure framework, this section will incorporate a generic narrative mountain structure, breaking down the plot by its background, rising action, climax, falling action, and resolution.
During each quarter, the project team worked with each TSO to produce an implementation plan including a set of targets, infographics, quality checklists, report templates and mentorship support, with PDSA cycles to structure the process [8, 9, 10]. Many of these tools required regular, in-depth auditing of data recorded during assessment, treatment, and discharge. Analyses were complemented by attitudinal surveys to front-line practitioners focusing on their perceptions and experiences across each quarter. Services were encouraged to reflect and communicate their learning at the quarterly residential meetings, while critically appraising fellow member’s contributions.
Throughout the project, it became clear that an organisation’s success in addressing the challenges depended on their relationship with the process of using measurement questionnaires and how deeply practitioners and clients were engaged in responding to feedback. The team later conceptualised this as a development cycle with four distinct evolutionary stages that described the operational depth of practitioners’ relationship with measurement: Pre and post-therapy measurement using paper forms; measurement at every session using paper forms; digital measurement at every session using tablets or computers; and digital measurement at every session tracking and sharing outcome progress directly with clients throughout the entire therapeutic encounter. It was recognised that services which were further along in this cycle had an inverse relationship with measurement in terms of its input and value towards stakeholders. Those in the later stages were able to maximise the value for clients that in turn benefitted other groups including practitioners, service management, and boards/funders. Conversely, those operating in the earlier stages were limited in their value to certain groups, typically to the boards/funders. Figure 2 shows a conceptual model of this, including the resulting value for stakeholders.
The evolutionary stages of measurement within SILC TSOs illustrating the development cycle and value to stakeholders.
Conceptual implementation models highlight how the structures and processes that exist within organisations have an influence on the adoption of practice changes during the active implementation phases [8, 10, 11]. Within the SILC project, it was observed that completing paper forms, particularly at every session, generated huge administrative and inefficient burdens for members. This created barriers for practitioners looking to use data as feedback to enhance client outcomes and develop their clinical skills. During the year, most organisations evolved their administrative processes by replacing paper with digital methods, recording via electronic tablets. The services most successful in achieving the optimal rates for each quarterly challenge described understanding measurement as a construct and extension of the client. By focusing on creating the maximum value of measurement for clients, a myriad of other benefits at different stakeholder levels was also reported [19]. Naturally, some services were more equipped than others in accessing the appropriate technologies.
During the project, one of the participating TSOs withdrew due to a turnover in management and evolving financial pressures. Two other services experienced management turnover during the project which, although not impacting on their participation, did require additional input and training from the SILC project team. Practitioner turnover was understood to be common in TSOs [2, 3, 4], however, the rate of turnover concentrated at a managerial level had not been anticipated. For services with a complex management structure, this too complicated the sharing of learning and addressing each quarterly challenge. It was discovered that when managers with an on-hand leadership style were absent, this would impact on key aspects of their service operation, including the collection of high-quality data.
Another key challenge regarded the issue of session attendance and unplanned endings. A list of categorical reasons for why a session was not attended was compiled to record each time this occurred. Although the reasons recorded for cancellations were high, this was not the case for those who did not attend (DNA) (no advanced warning given) despite subsequent sessions being attended in approximately half of all instances. The most common reason for cancellations during the second quarter (n = 482) was ‘Health Problems’ (40%) while for DNAs (n = 160) it was ‘Unknown’ or ‘Not Recorded’ (76%). The absence of reasons recorded despite sessions being subsequently attended suggests practitioners either forgot or did not feel comfortable exploring why a session had been missed. This is concerning as DNAs were found to be indicative of an unplanned ending.
Definitions are important and have shown to vary the reported unplanned ending rate [20]. During the project, the unplanned ending rate reduced from 32% at baseline to 27% at the end of the third quarter, however defining and interpreting these rates revealed notable issues. Among the participating members, there were multiple interpretations about what constituted a planned versus unplanned ending. Given its inherently subjective nature and potentially negative connotations, this limited the analysis somewhat. However, the links between session non-attendance and unplanned endings were consistent across all services and tended to occur early in treatment, as described in the next section.
One of the aims of the SILC project was to provide services with regular analyses to inform delivery and operation. This section reports on some of the headline findings along with extract quotes from two of the SILC TSOs. Systems-level modelling demonstrates the importance of considering the interrelationships between individual practice elements as opposed to solely focusing on each in isolation [11, 21]. Although the challenges during each quarter were distinct, the areas of overlap were noteworthy. Not only was session non-attendance linked with unplanned endings, but those TSOs with the longest standing commitment to high-quality data also reported the highest rates of clinical improvement.
One major shift during the first quarter was to adopt sessional ROM, moving from traditional pre and post-therapy measurement approaches. This process was supported by a dedicated project member auditing and feeding back information to services. By the end of the first quarter, pre-and-post outcome completion rates increased from an average of 65% at baseline to 98%, while by the end of the year, this was 97%, with all TSOs achieving above 90% and half achieving 100% completion rates (Figure 3). These values were almost identical to the IAPT programme’s recent achievement of 98%, a decade after its first site implementation [7].
Improvement of pre-and-post outcome measures completion rates for all SILC TSOs, 1 year before-and-after the project.
At the start of the second quarter, members began to record session non-attendance, including when an appointment was cancelled (by client) or the client DNA (no advanced warning given). One of the primary areas of interest was understood when sessions being missed were most likely to occur. Aggregating each service’s datasets, the total number of appointments per sequential session number was tallied to assess what proportion was recorded as either cancelled or DNA. Including only session numbers with over 10 appointments each, it was possible to chart this data (Figure 4). It was identified that cancellations as a proportion tended to increase the longer therapy progressed; although this might be due to a lower number of appointments at these stages. DNAs as a proportion did not exceed 10% for any session number although they did tend to occur earlier in therapy, with sessions 2–5 reporting the highest rates of 7–8%. The occurrence of DNAs declined somewhat as therapy progressed, possibly due to contracting which discharged clients after missed appointments without prior notice. Focusing on session non-attendance helped determine the scale of the challenge and how the pattern of cancellations and DNAs differed, prompting two participating services to a revise their policy in the interests of equitable access and service efficiency.
The rate of appointment non-attendance per session number showing a higher proportion of DNAs earlier and cancellations later in therapy, across all SILC TSOs.
For the third quarter, the focus shifted to exploring the nature of unplanned endings. An analysis was undertaken to explore the potential associations between unplanned endings and the rate of non-attendance during therapy. This analysis found that, across all services, there was a link between session absence and ultimate attrition, especially regarding DNAs. For all TSOs, the DNA rate for clients with an unplanned (13%) versus planned (2%) ending was around 6½ times difference, ranging from 2 to 18 times across providers (Figure 5). By the end of the third quarter, those with planned endings attended almost 3 times more sessions (11) than those with unplanned endings (4) and were more likely to report reliable improvement for planned (62%, n = 226) versus unplanned (36%, n = 70) endings.
A comparison of session non-attendance reporting a higher rate for unplanned versus planned endings across all SILC TSOs.
To assess how the pattern of non-attendance varied during therapy per ending type, session numbers and total appointments recorded were banded across all services (Figure 6). This analysis found that again, non-attendance was indicative of an unplanned ending, with higher rates of cancellations and DNAs. For those with an unplanned ending, it also revealed that while DNAs as a proportion were reduced in the lower session number bandings (2–4; 5%), they remained consistent at around 17–21%, excluding the 14–16 banding which reported a rate of 30%. Similar to the overall patterns of attendance, cancellations as a proportion of all appointments tended to increase the longer therapy progressed but again, this could be explained by a decrease in appointments recorded during these later subgroup stages.
A comparison of session non-attendance bandings showing a steady DNA rate and increasing cancellations for unplanned versus planned endings, across all SILC TSOs.
In the final quarter, the project focused on clinical outcomes and understanding therapist variation and trajectories of change. To identify a possible dose-effect, an analysis was undertaken to assess the rates of change across individual domains of the CORE-OM (wellbeing, problems, functioning, and risk) within the one service using the full 34-item measure, as opposed to the shorter CORE-10 which does not record all domains [17]. A pattern of average scores were mapped relative to individual session numbers up to the 10th session (for clients having 10+ appointments each) for those who reported reliable improvement (n = 130; 891 sessions) versus those who reported no reliable change (n = 39 clients; 243 sessions) or reliable deterioration (n = 7 clients; 53 sessions) (Figure 7). Based on this analysis, most of the score changes tended to occur early in treatment for those reporting reliable improvement, with an average decrease in scores of −6.1 across the first four sessions, remaining steady between sessions four to seven (−0.5), and then decreasing steadily from sessions seven to 10 (−2.3). For those reporting no reliable change or reliable deterioration, scores generally remained steady, with average changes ranging from 0.2 to 1.7. This suggests the first four sessions were important for identifying clients who were likely to improve or not. This triggered the integration of a flag feature to remind practitioners to review progress early in therapy to identify those at-risk of showing no change to provide additional support.
A pattern-of-change comparison across the CORE-OM per session number illustrating early improvements for clients reporting reliable improvement compared with no reliable change or reliable deterioration.
Informed by the QIF, improvement for future applications requires learning from experience [8]. To gauge the experiences of those participating in the project, a brief semi-structured interview was conducted at the end of the year to explore what service managers thought of the initiative, and how they might improve it for future services embarking on a similar journey of collaborative learning. The boxes below contain extracts from these interviews with two self-selected TSOs.
Service A: Interview Extracts |
Our first question was how is it going to work for our clients? Building that value for them, and the practitioners, giving them a value to the work. This is not a measurement, it’s not an outcome, it’s an aide to the process, something that helps the work with clients. And then, once we all understood that, we could have an open conversation about why we might want something like this. You really need that opportunity to embed it early on though. It completely allowed us to cement and consolidate how we work. I mean the data the project provided, really cemented what we were doing, how we were doing, we were using data in the right way, but it also gave us ways to look at data differently, what we could do, so it was an enhancing experience. That allowed us to feel quite proud of what we do, and have it validated, which for us a charity tucked away from others, that was a nice thing to have it validated on that level. I did that like kind of cyclical journey, that it’s not linear, we’ve got new practitioners all the time, we’ve just got 8 new practitioners in now, and they’re going back through that loop. They’re doing their first data clean this week where I’m just putting them through all the information, right we need to go through and see, right this is done, this is done, and you keep on embedding it, keeping the data quality up really. Constant, it must be really because when I’ve dipped out of the service, it went a little bit, my practitioners got a little bit complacent. I think one of the biggest things for us, the 4-session thing, spotting that. We actively use that in supervision now, so it’s really looking at, from that first session, you can see it quite clearly. So, there’s more focus in those first 4 sessions, really looking at what the client needs, with a view to contracting through goals, further through that process. So that we’re really meeting those needs, making that environment that’s conducive then to achieving good outcomes. We’re about sharing good practice, we’re about empowerment, we’re about creating choice and all those things. Being part of SILC fitted with part of the ethos so it was nice to go and be there in that capacity with other services. There’s something about talking to someone who’s been through it, we’re just through it. It’s that kind of picking their brains and have you thought this? For me it’s about credentialing the sector, it’s about professionalism, it’s about best practice, it’s about evidence base, not being afraid to strive, to get to those levels, and get good outcomes and be accountable for that. I don’t think therapy is any different from if you go to a shop to buy something you expect it to be good quality. I don’t see why in therapy, clients shouldn’t expect it to be any different. |
Service E: Interview Extracts |
Having the support from the team that was specific to our service, having experts on hand when you needed them. Keeping on top of the data quality is not as easy without the help of the project team, and our monthly calls and the little tool pointing out the problems… Whereas sitting down and finding the problems myself is another matter. I personally enjoy getting involved in things like this. I find it very stimulating. It ticked a lot of boxes for me, in terms of what we wanted for the service, but also for me personally, it was an interest. You couldn’t have designed it better for me… So, I think without that personal interest and enthusiasm it wouldn’t have happened. I think I’m very fortunate in that I’ve got a very good group of people, I think credit needs to go where it\'s due, they are a group of people who are motivated and supportive and I think all we did was talk about, well this is going to be a benefit to the service, and they’re all very committed to the service and they came, I suppose, with open minds. That’s been one of the key things for me, has been the experience of being part of the learning collaborative. And I think that is so valuable, personally and also for the service, because you’re going through a journey with other services, their journey’s different but there are similar issues. It’s just that ability to share learning and connect with people who have a similar job, are having similar issues. When you have something, and they say, yeah that’s happened to me. And for me, it takes away that sense of being in your own little bubble, in your own little service, which I wouldn’t say is isolating but that you’re not part of anything else. The learning collaborative made you feel part of something bigger with some connections, and yeah, doing the same thing you’re doing, I thought it’s fabulous, it’s brilliant. It’s the practical stuff, we’ve become a service that does sessional measurement using tablets, that’s the way we do things now. We’ve changed the way we manage DNAs, we have an appreciation of data quality, and that’s not just me, the team come along and say why haven’t I got 100%? Why is this only saying 90%? Can we have a look where that 10% has gone? So, there is an appreciation now of the importance of good data. In fact, the things that SILC was meant to address, are the things that have changed in our service. It’s a no-brainer. Why wouldn’t you? I can’t see any reason why you wouldn’t, unless you haven’t got the support to see it through. Know your organisation, know that you’ve got that support, to be able to put the time into it, those are the two caveats, otherwise, it’s a no-brainer. |
In keeping with the IHI’s [10] collaborative learning model framework, the first year of the SILC project culminated in a summative conference. Nearly 100 delegates were in attendance, each representing a range of different sectors within the field of talking therapies. Both the project team and self-selected SILC TSOs held a discussion regarding their experiential learning during the first year of the project. There was a consensus at the event about the operational challenges facing modern-day talking therapy services. While systems were becoming increasingly sophisticated, the training and support necessary to build in-house expertise were reportedly difficult to access due to time and resource constraints, a saturated and uncertain field, and isolated working practices. Providers, particularly in the third-sector, desired the opportunity to work in partnership with others to share learning and enhance theirs and the sector’s organisational and therapeutic models further.
With the first stage complete, the SILC project has amassed a wealth of learning which will be converted into a modular learning programme, providing a resource for future applications of the network [8, 9]. This will replicate the CLN model and invite existing SILC members to act as guest speakers and offer unique support and valuable insights to newly recruited collaborative members. There are three existing SILC TSOs who have declared their interest and commitment to continuing with the project. Due to a turnover in management and decrease in contribution, two members have since withdrawn. The next phase of the initiative will focus on expanding the network, building on the existing knowledge and aggregate data to support ongoing analyses and resource development.
Themes are the essence of a story, the central constructs which reflect the actions, perceptions and experiences of the characters in their situational contexts. They represent the underlying ‘big ideas’ which transcend the distinctions between settings and circumstances and help conceptualise elements and links between them. This is important given the lack of guiding conceptual models for the sustainment phase of implementation [11]. Listed below is a discussion on some of the key themes both the participating services and project team uncovered during this stage of the project.
The unintegrated nature of TSOs in the UK means there can be obstructions to developing and integrating EBP [2, 3, 4]. Within the field of talking therapies, determining what constitutes as EBP has been criticised for its reliance on controlled study methodologies which, due to their somewhat artificial nature, are considered detached from the clinical realities of routine practice settings [22, 23]. Certain advocates support a PBE approach to complement and address these limitations [24]. However, PBE relies on the collection of robust, aggregate datasets across multiple organisations sharing a common system or model.
Fragmentation, isolated working practices, and resource constraints can limit TSOs generating the PBE necessary to support their delivery [2, 3, 4]. Indeed, the primary interest from prospective members in this project was overcoming these barriers and demonstrating they were treating clients effectively. By pooling experience, resources and expertise around a central, unifying theme, TSOs were able to systematically explore, assess, understand and reflect upon key aspects of service quality development. Through iterative cycles, strategic improvement models and coordinated and collaborative dialogue [10], services were able to generate timely and actionable insights that were relevant to their unique circumstances. Testing practice changes on small scales, using focused inquiry and PDSA cycles, helped achieve small wins which, according to evaluation theories, can be an effective strategy for boosting perceived capabilities [6, 10].
Replicating previous research findings [2, 3], access to a supportive academic project team was deemed invaluable for producing, mentoring and synthesising analyses and learning across the network. However, liaising with several TSOs proved to be a lengthier and more complicated process than first envisaged; an experience which is echoed elsewhere [6]. This identifies an important obstacle for sustaining CLNs, particularly those undertaking continuous analyses. It might be that by offsetting resources to a project team, this creates a more efficient process within individual services as it shares the expertise around a common need. If this were true, then it could prove more efficient and cost-effective for TSOs overall.
Given the central communicative nature of CLNs, it is important these channels are equitable. Within the third-sector, organisations tend to differ in size and can be equally varied in their operational modelling [2, 3]. This inequity in size and visibility could feasibly leverage greater influence over smaller providers to work towards their agenda. To overcome the challenges of distinct delivery models within CLNs, a central governing platform using cooperative representation could therefore be valuable for identifying topics of interests and establishing a dictionary of terms. Similarly, these communication channels ought to use terminology that is consistent and agreed upon, particularly around subjective concepts such as ending types as doing so would ensure greater validity and reliability in data analytics [20].
Many implementation frameworks emphasise the planning stages as critical to successfully embedding innovation [8, 11, 12, 13]. Because implementation can be a complex process involving integrating existing practices with new, it typically requires a well-planned, structured and iterative process, addressing the various philosophical and practical barriers that can occur regularly [9, 15, 21]. It is within these contexts that supportive leadership can be a facilitating factor [2, 11, 15, 21, 25]. Without effective leadership to track, monitor and effectively champion the merging of practices, any expended effort can unravel [9, 15, 25]. Those in leadership positions need to be present and well-respected, retaining a detailed awareness and understanding of delivery and operation [15]. Service quality development through CLNs therefore appears to be reliant on management structures and local leadership.
In considering the scale of change and level of turnover in TSOs, particularly at a managerial level, the reliance on leadership highlights a notable barrier. Given the project team tended to work exclusively through managers brokering knowledge and training, their absence ultimately affected their organisation’s participation and operational processes. It could be argued this was a side-effect of the chosen methodology which may have benefitted from a broader involvement and contribution among the workforce. Advocates across the field recommend ensuring a local champion is permanently in place, advising that those departing a service provide sufficient training to those replacing them [9, 15, 26]. While this recommendation is practical, how it applies to TSOs is perhaps more complicated.
Continually nurturing the operational climate through sustained involvement and being present can help resolve the functional mechanisms of feedback systems [15, 25, 26, 27, 28]. A perceived lack of presence in the project among some practitioners served to undermine the initial enthusiasm and positive ethos established at the project’s outset. Services which thrived tended to dedicate additional time and resources to sharing information in an open and accessible manner. This actively engaged the workforce in the minutiae of feedback informed treatment (FIT) [28] and encouraged more open dialogue. The literature on FIT teaches the value of routinely soliciting responses from clients about treatment progress, aiding practitioners at a therapeutic level [28, 29, 30]. However, there is an additional service-level which could also help inform practitioners and other stakeholders about enhancing client engagement and outcomes. By combining a FIT model with a feedback informed service, practitioners could have timely access to relevant learning. With reference to the QIF [8], supportive feedback mechanisms will be relevant to all stakeholder levels and through aggregate data, the client voice can be made accessible to all, helping sustain innovation.
Based on the learning from this initiative and relevant national and international research [2, 3, 4], there appears to be a significant resource challenge facing TSOs. Although many report having an interest in quality improvement [3], the constraints on providers including turnover, financial pressures and limited budgets, appear to greatly impact their ability to generate data and engage in practice development [2, 3, 4]. For a sector that relies heavily on volunteers, some of whom are in trainee positions [1], preserving a level of local expertise represents a continual challenge, particularly as systems become more expansive, specialised and costly. Although the CLN was a means to pool and share resources, supporting the implementation phase [11], external pressures had a notable influence on its integration, process and overall output. The level of attrition at the beginning and eventual withdrawal of others highlights the scale of this challenge. Consequently, this further demonstrates the criticality of the QIF phases in thoroughly assessing the fit between the host setting’s aspirations and readiness for change [8, 9].
Given the sheer scale of change and advancing pace of new technologies, feedback systems and innovations are becoming increasingly sophisticated while at the same time, access to training and support might not be keeping pace [3, 31]. For many, including attendees at the summative conference and across the wider literature [3], allocating resources to this endeavour might be considered non-feasible as few can afford or justify it economically. This issue is further compounded by the fluctuating and isolated nature of services as well as barriers in accessing the literature due to subscription paywalls [2, 3]. Accordingly, this highlights the need to consider the additional training and support required when adopting new innovations.
Despite its limitations, a CLN could address some of the resource challenges identified, increasing the opportunities for learning. Disseminating feedback throughout a network might help overcome some of the barriers to accessing research and forming partnerships [5, 6, 10]. Shared learning across all levels of the network, could foster a broader culture of openness and training, supporting collaboration across multiple platforms, while also generating an asset for feeding back insights across the sector. Undoubtedly, this would rely on the aggregation of robust datasets and communication platform to support this process [5].
The experiences from this project revealed the influence of organisational factors and infrastructure on the uptake of practice changes. Although research on the integration of feedback systems and ROM have identified numerous practical barriers, much of the emphasis has focused on practitioners [9, 15, 31, 32, 33, 34, 35, 36, 37]. Indeed, positive attitudes towards feedback have been shown to facilitate the effect on clinical outcomes improvement, while resistance can have the opposite effect [33, 38, 39, 40]. Resistance reportedly stems underlying performance anxiety or negativity about the relevance and utility of the practice [9, 15]. However, the learning from this project highlights how positivity and motivation might not be sufficient in isolation.
Despite the generally positive attitudes from the survey and among the management mentees, itself likely a result of the selection process, many TSOs still encountered challenges, many of which appeared to be due to limitations in the infrastructure and frustrations with the technology. This, in turn, affected their capacity to use the system, something which is shown to be a facilitator in implementing EBP [25, 27, 31]. Restrictive and frustrated working practices can lead to negative perceptions forming [25, 27, 36, 41], suggesting attitudes might be mediated by how user-friendly and engaging a system is. For TSOs facing time and resource constraints, the simplicity of a feedback system is perhaps more pivotal. In these circumstances, systems may benefit from a uniform, standardised approach so that training and support can be refined and accessible via fully integrated and self-led instructional packages [32]. In terms of the QIF [8], the critical steps for assessing the needs and resources, capacity, and pre-implementation training would benefit from accessible resources which are intuitive and easy to understand.
Traditionally, measurement in TSOs have been undertaken to satisfy the needs of boards and funders and to a lesser extent, service managers [3, 4]. The pressures on services have meant that pre and post-measurement approaches have dominated, with its purpose serving mainly administrative rather than clinical needs [3, 9]. ROM established a method for improving data quality and representativeness, although the emphasis regarding its clinical utility or use in service development has only recently been advanced [7]. This illustrates how the focus and value of measurement have been positioned to satisfy a broader sector-level drive. However, by framing measurement in a way to maximise the value for clients, as observed in this project, there appear to be many cumulative gains for all stakeholders, including practitioners, service managers and boards/funders.
Across each of the common challenges, there seemed to be a critical period, usually within the first four to six sessions, which correlated with eventual outcome. For instance, a large proportion of DNAs tended to occur early in treatment which were a useful indicator of an unplanned ending, and by extension, a reduced chance of reliable improvement [20]. For clients reporting reliable improvement in one TSO, most change seemed to occur during the first four sessions, while those reporting no reliable change or reliable deterioration showed little change across a 10-session period. This emulates the wider literature which identifies the initial stages as being a useful indicator for a client’s subsequent engagement and outcome [42, 43, 44, 45]. Accordingly, this trend highlights the criticality of early engagement and warrants a further discussion about the implications of keeping clients involved in therapy who report no change or attend infrequently. Evidence has shown that decisions to prolong or conclude therapy despite a lack of positive therapeutic change can be influenced by subjective beliefs, norms and attitudes, sometimes superseding what feedback monitoring and practice guidelines recommend [45].
According to the literature, the clinical benefit of measurement can be mediated by a practitioner’s engagement and attitude towards outcomes monitoring [33, 38, 39]. Moreover, timely access to feedback has been shown to be a critical factor in the use of data among practitioners [27, 34, 36, 46]. TSOs which encourage open dialogue and pay greater attention to this information could produce cumulative benefits in each of the quarterly themes identified [10, 30, 47]. An organisational culture of openness and commitment to learning was important and replicates findings reported elsewhere [15, 46]. Additionally, giving practitioners access to service-level data might assist them in overcoming residual ambivalence because its application to service quality development is readily observable.
For those interested in implementing a CLN to support TSOs, there are several recommendations based on this project’s findings. Firstly, recording high-quality data is crucial to this model. Securing high-quality data helps support the network and aggregate learning by effectively threading the client voice throughout all stakeholder levels. Promoting client engagement in the process of measurement is an effective strategy for enhancing data quality and building the opportunities for clinical application [9, 31, 47]. Because of this, it is important that implementation teams do not underestimate the infrastructure necessary to support practitioners working to deliver these innovations [15, 32, 35, 46]. While pooling resources can help overcome challenges relating to cost and access to expertise, without a shared framework and understanding of the key concepts, a CLN and its associated analyses are likely to be impacted. In keeping with the wider literature, access to expertise and committed project team can be beneficial for supporting the network [2, 3, 5, 6, 9]. Focusing on distinct areas of service delivery through iterative improvement cycles and acknowledging their interdependency can help achieve cumulative benefits through the combination of smaller gains [6, 21, 25]. For TSOs, the role of leadership and effects of turnover cannot be understated. While it might not be feasible in TSOs to ensure a local champion is always in place, it is valuable to build a system that enables receptiveness towards continual practice innovation. A broader involvement and contribution among the workforce through wider supportive feedback mechanisms represents one effective strategy to overcome this.
TSOs represent a valuable and growing player in the provision of mental health care, yet many are constrained by limited budgets, isolated working practices, and a constantly shifting workforce. Together, these make producing and accessing evidence difficult, further limiting the sector from credentialing their impact and engaging in service development. To overcome these challenges, a CLN was implemented involving six TSOs and a dedicated project team to share learning and resources with the aim of improving delivery and operation in the areas of data quality, session attendance, unplanned endings and clinical outcomes. The CLN was inspired by the IHI collaborative model [10] framework for integrating and testing improvements using PDSA cycles and the implementation process was guided by the QIF [8]. It was found that introducing ROM substantially improved data quality which acted as the bedrock for all subsequent analyses and discussion. There appeared to be strong links between each of the common challenges, including increased non-attendance being associated with the occurrence of an unplanned ending, itself linked with a lower chance of reliable improvement. Overall, this approach to generating timely and relevant practice-based insight through partnership working and mentorship support proved to be effective for stimulating service quality enhancement. Although TSOs face many unique challenges, including high staff turnover and strained budgets, those with on-hand and inspirational leadership and commitment towards maximising the value of measurement for clients reported most success.
This work was supported by the Artemis Trust (No grant number). The funder had no further role in the design, collection, analysis, interpretation and compilation of this paper and no financial interests or benefits have arisen from the direct applications of this research.
Scott Steen and John Mellor-Clark declare they have no conflicts of interest.
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\n\nThe Open Access Publishing Fee (OAPF) is payable only after your full chapter, monograph or Compacts monograph is accepted for publication.
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\n\n*These prices do not include Value-Added Tax (VAT). Residents of European Union countries need to add VAT based on the specific rate in their country of residence. Institutions and companies registered as VAT taxable entities in their own EU member state will not pay VAT as long as provision of the VAT registration number is made during the application process. This is made possible by the EU reverse charge method.
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