Learning Electronic Polarizations in Aqueous Systems.

The polarization of periodically repeating systems is a discontinuous function of the atomic positions, a fact which seems at first to stymie attempts at their statistical learning. Two approaches to build models for bulk polarizations are compared: one in which a simple point charge model is used to preprocess the raw polarization to give a learning target that is a smooth function of atomic positions and the total polarization is learned as a sum of atom-centered dipoles and one in which instead the average position of Wannier centers around atoms is predicted. For a range of bulk aqueous systems, both of these methods perform perform comparatively well, with the former being slightly better but often requiring an extra effort to find a suitable point charge model. As a challenging test, we also analyze the performance of the models at the air-water interface. In this case, while the Wannier center approach delivers accurate predictions without further modifications, the preprocessing method requires augmentation with information from isolated water molecules to reach similar accuracy. Finally, we present a simple protocol to preprocess the polarizations in a data-driven way using a small number of derivatives calculated at a much lower level of theory, thus overcoming the need to find point charge models without appreciably increasing the computation cost. We believe that the training strategies presented here help the construction of accurate polarization models required for the study of the dielectric properties of realistic complex bulk systems and interfaces with ab initio accuracy.

Gaussian Process Regression for Materials and Molecules.

We provide an introduction to Gaussian process regression (GPR) machine-learning methods in computational materials science and chemistry. The focus of the present review is on the regression of atomistic properties: in particular, on the construction of interatomic potentials, or force fields, in the Gaussian Approximation Potential (GAP) framework; beyond this, we also discuss the fitting of arbitrary scalar, vectorial, and tensorial quantities. Methodological aspects of reference data generation, representation, and regression, as well as the question of how a data-driven model may be validated, are reviewed and critically discussed. A survey of applications to a variety of research questions in chemistry and materials science illustrates the rapid growth in the field. A vision is outlined for the development of the methodology in the years to come.

A fully quantum-mechanical treatment for kaolinite.

Neural network potentials for kaolinite minerals have been fitted to data extracted from density functional theory calculations that were performed using the revPBE + D3 and revPBE + vdW functionals. These potentials have then been used to calculate the static and dynamic properties of the mineral. We show that revPBE + vdW is better at reproducing the static properties. However, revPBE + D3 does a better job of reproducing the experimental IR spectrum. We also consider what happens to these properties when a fully quantum treatment of the nuclei is employed. We find that nuclear quantum effects (NQEs) do not make a substantial difference to the static properties. However, when NQEs are included, the dynamic properties of the material change substantially.

Hydrogen-bonding and nuclear quantum effects in clays.

Hydrogen bonds are of paramount importance in the chemistry of clays, mediating the interaction between the clay surface and water, and for some materials between separate layers. It is well-established that the accuracy of a computational model for clays depends on the level of theory at which the electronic structure is treated. However, for hydrogen-bonded systems, the motion of light H nuclei on the electronic potential energy surface is often affected by quantum delocalization. Using path integral molecular dynamics, we show that nuclear quantum effects lead to a relatively small change in the structure of clays, but one that is comparable to the variation incurred by treating the clay at different levels of electronic structure theory. Accounting for quantum effects weakens the hydrogen bonds in clays, with H-bonds between different layers of the clay affected more than those within the same layer; this is ascribed to the fact that the confinement of an H atom inside a layer is independent of its participation in hydrogen-bonding. More importantly, the weakening of hydrogen bonds by nuclear quantum effects causes changes in the vibrational spectra of these systems, significantly shifting the O-H stretching peaks and meaning that in order to fully understand these spectra by computational modeling, both electronic and nuclear quantum effects must be included. We show that after reparameterization of the popular clay forcefield CLAYFF, the O-H stretching region of their vibrational spectra better matches the experimental one, with no detriment to the model's agreement with other experimental properties.

Accurate molecular polarizabilities with coupled cluster theory and machine learning.

The molecular dipole polarizability describes the tendency of a molecule to change its dipole moment in response to an applied electric field. This quantity governs key intra- and intermolecular interactions, such as induction and dispersion; plays a vital role in determining the spectroscopic signatures of molecules; and is an essential ingredient in polarizable force fields. Compared with other ground-state properties, an accurate prediction of the molecular polarizability is considerably more difficult, as this response quantity is quite sensitive to the underlying electronic structure description. In this work, we present highly accurate quantum mechanical calculations of the static dipole polarizability tensors of 7,211 small organic molecules computed using linear response coupled cluster singles and doubles theory (LR-CCSD). Using a symmetry-adapted machine-learning approach, we demonstrate that it is possible to predict the LR-CCSD molecular polarizabilities of these small molecules with an error that is an order of magnitude smaller than that of hybrid density functional theory (DFT) at a negligible computational cost. The resultant model is robust and transferable, yielding molecular polarizabilities for a diverse set of 52 larger molecules (including challenging conjugated systems, carbohydrates, small drugs, amino acids, nucleobases, and hydrocarbon isomers) at an accuracy that exceeds that of hybrid DFT. The atom-centered decomposition implicit in our machine-learning approach offers some insight into the shortcomings of DFT in the prediction of this fundamental quantity of interest.

Determination and evaluation of the nonadditivity in wetting of molecularly heterogeneous surfaces.

The interface between water and folded proteins is very complex. Proteins have "patchy" solvent-accessible areas composed of domains of varying hydrophobicity. The textbook understanding is that these domains contribute additively to interfacial properties (Cassie's equation, CE). An ever-growing number of modeling papers question the validity of CE at molecular length scales, but there is no conclusive experiment to support this and no proposed new theoretical framework. Here, we study the wetting of model compounds with patchy surfaces differing solely in patchiness but not in composition. Were CE to be correct, these materials would have had the same solid-liquid work of adhesion (WSL ) and time-averaged structure of interfacial water. We find considerable differences in WSL , and sum-frequency generation measurements of the interfacial water structure show distinctively different spectral features. Molecular-dynamics simulations of water on patchy surfaces capture the observed behaviors and point toward significant nonadditivity in water density and average orientation. They show that a description of the molecular arrangement on the surface is needed to predict its wetting properties. We propose a predictive model that considers, for every molecule, the contributions of its first-nearest neighbors as a descriptor to determine the wetting properties of the surface. The model is validated by measurements of WSL in multiple solvents, where large differences are observed for solvents whose effective diameter is smaller than ∼6 Å. The experiments and theoretical model proposed here provide a starting point to develop a comprehensive understanding of complex biological interfaces as well as for the engineering of synthetic ones.

Attachment goes to court: child protection and custody issues.

Attachment theory and research are drawn upon in many applied settings, including family courts, but misunderstandings are widespread and sometimes result in misapplications. The aim of this consensus statement is, therefore, to enhance understanding, counter misinformation, and steer family-court utilisation of attachment theory in a supportive, evidence-based direction, especially with regard to child protection and child custody decision-making. The article is divided into two parts. In the first, we address problems related to the use of attachment theory and research in family courts, and discuss reasons for these problems. To this end, we examine family court applications of attachment theory in the current context of the best-interest-of-the-child standard, discuss misunderstandings regarding attachment theory, and identify factors that have hindered accurate implementation. In the second part, we provide recommendations for the application of attachment theory and research. To this end, we set out three attachment principles: the child's need for familiar, non-abusive caregivers; the value of continuity of good-enough care; and the benefits of networks of attachment relationships. We also discuss the suitability of assessments of attachment quality and caregiving behaviour to inform family court decision-making. We conclude that assessments of caregiver behaviour should take center stage. Although there is dissensus among us regarding the use of assessments of attachment quality to inform child custody and child-protection decisions, such assessments are currently most suitable for targeting and directing supportive interventions. Finally, we provide directions to guide future interdisciplinary research collaboration.

Predicting molecular dipole moments by combining atomic partial charges and atomic dipoles.

The molecular dipole moment (µ) is a central quantity in chemistry. It is essential in predicting infrared and sum-frequency generation spectra as well as induction and long-range electrostatic interactions. Furthermore, it can be extracted directly-via the ground state electron density-from high-level quantum mechanical calculations, making it an ideal target for machine learning (ML). In this work, we choose to represent this quantity with a physically inspired ML model that captures two distinct physical effects: local atomic polarization is captured within the symmetry-adapted Gaussian process regression framework which assigns a (vector) dipole moment to each atom, while the movement of charge across the entire molecule is captured by assigning a partial (scalar) charge to each atom. The resulting "MuML" models are fitted together to reproduce molecular µ computed using high-level coupled-cluster theory and density functional theory (DFT) on the QM7b dataset, achieving more accurate results due to the physics-based combination of these complementary terms. The combined model shows excellent transferability when applied to a showcase dataset of larger and more complex molecules, approaching the accuracy of DFT at a small fraction of the computational cost. We also demonstrate that the uncertainty in the predictions can be estimated reliably using a calibrated committee model. The ultimate performance of the models-and the optimal weighting of their combination-depends, however, on the details of the system at hand, with the scalar model being clearly superior when describing large molecules whose dipole is almost entirely generated by charge separation. These observations point to the importance of simultaneously accounting for the local and non-local effects that contribute to µ; furthermore, they define a challenging task to benchmark future models, particularly those aimed at the description of condensed phases.

Inexpensive modeling of quantum dynamics using path integral generalized Langevin equation thermostats.

The properties of molecules and materials containing light nuclei are affected by their quantum mechanical nature. Accurate modeling of these quantum nuclear effects requires computationally demanding path integral techniques. Considerable success has been achieved in reducing the cost of such simulations by using generalized Langevin dynamics to induce frequency-dependent fluctuations. Path integral generalized Langevin equation methods, however, have this far been limited to the study of static, thermodynamic properties due to the large perturbation to the system's dynamics induced by the aggressive thermostatting. Here, we introduce a post-processing scheme, based on analytical estimates of the dynamical perturbation induced by the generalized Langevin dynamics, which makes it possible to recover meaningful time correlation properties from a thermostatted trajectory. We show that this approach yields spectroscopic observables for model and realistic systems that have an accuracy comparable to much more demanding approximate quantum dynamics techniques based on full path integral simulations.

Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems.

Statistical learning methods show great promise in providing an accurate prediction of materials and molecular properties, while minimizing the need for computationally demanding electronic structure calculations. The accuracy and transferability of these models are increased significantly by encoding into the learning procedure the fundamental symmetries of rotational and permutational invariance of scalar properties. However, the prediction of tensorial properties requires that the model respects the appropriate geometric transformations, rather than invariance, when the reference frame is rotated. We introduce a formalism that extends existing schemes and makes it possible to perform machine learning of tensorial properties of arbitrary rank, and for general molecular geometries. To demonstrate it, we derive a tensor kernel adapted to rotational symmetry, which is the natural generalization of the smooth overlap of atomic positions kernel commonly used for the prediction of scalar properties at the atomic scale. The performance and generality of the approach is demonstrated by learning the instantaneous response to an external electric field of water oligomers of increasing complexity, from the isolated molecule to the condensed phase.

Fast-forward Langevin dynamics with momentum flips.

Stochastic thermostats based on the Langevin equation, in which a system is coupled to an external heat bath, are popular methods for temperature control in molecular dynamics simulations due to their ergodicity and their ease of implementation. Traditionally, these thermostats suffer from sluggish behavior in the limit of high friction, unlike thermostats of the Nosé-Hoover family whose performance degrades more gently in the strong coupling regime. We propose a simple and easy-to-implement modification to the integration scheme of the Langevin algorithm that addresses the fundamental source of the overdamped behavior of high-friction Langevin dynamics: if the action of the thermostat causes the momentum of a particle to change direction, it is flipped back. This fast-forward Langevin equation preserves the momentum distribution and so guarantees the correct equilibrium sampling. It mimics the quadratic behavior of Nosé-Hoover thermostats and displays similarly good performance in the strong coupling limit. We test the efficiency of this scheme by applying it to a 1-dimensional harmonic oscillator, as well as to water and Lennard-Jones polymers. The sampling efficiency of the fast-forward Langevin equation thermostat, measured by the correlation time of relevant system variables, is at least as good as the traditional Langevin thermostat, and in the overdamped regime, the fast-forward thermostat performs much better, improving the efficiency by an order of magnitude at the highest frictions we considered.

Indoor air bacterial communities in Hong Kong households assemble independently of occupant skin microbiomes.

More than in any other habitat, humans exert a large influence on microbial communities indoors. Frequent contact between occupant skin and indoor surfaces causes indoor surface microbial communities to be largely assembled from and thus closely resemble occupant skin microbiomes. While indoor air and dust are known to also contain many human-associated taxa, household air communities have not yet been directly compared with occupant skin microbiomes. We sampled microorganisms from air, surfaces and occupant skin in 19 Hong Kong households and used Illumina sequencing of the V4 hypervariable region of the 16S rRNA gene to investigate the dispersal relationships between the bacterial communities at each site. Our results confirmed that indoor surfaces bear the 'bacterial fingerprint' of household occupant skin. However, while air communities contained abundant human-associated taxa and were household specific, air communities in each household did not resemble occupant skin from that household any more than occupant skin from other households. Our results suggest that, at least in Hong Kong, indoor air bacterial communities may be assembled largely from outdoor air and occupant body sites other than skin, and unlike surface communities do not harbour the occupants' skin 'bacterial fingerprint'.

Crystal structures of the Toll/Interleukin-1 receptor (TIR) domains from the Brucella protein TcpB and host adaptor TIRAP reveal mechanisms of molecular mimicry.

The Toll/IL-1 receptor (TIR) domains are crucial innate immune signaling modules. Microbial TIR domain-containing proteins inhibit Toll-like receptor (TLR) signaling through molecular mimicry. The TIR domain-containing protein TcpB from Brucella inhibits TLR signaling through interaction with host adaptor proteins TIRAP/Mal and MyD88. To characterize the microbial mimicry of host proteins, we have determined the X-ray crystal structures of the TIR domains from the Brucella protein TcpB and the host adaptor protein TIRAP. We have further characterized homotypic interactions of TcpB using hydrogen/deuterium exchange mass spectrometry and heterotypic TcpB and TIRAP interaction by co-immunoprecipitation and NF-κB reporter assays. The crystal structure of the TcpB TIR domain reveals the microtubule-binding site encompassing the BB loop as well as a symmetrical dimer mediated by the DD and EE loops. This dimerization interface is validated by peptide mapping through hydrogen/deuterium exchange mass spectrometry. The human TIRAP TIR domain crystal structure reveals a unique N-terminal TIR domain fold containing a disulfide bond formed by Cys(89) and Cys(134). A comparison between the TcpB and TIRAP crystal structures reveals substantial conformational differences in the region that encompasses the BB loop. These findings underscore the similarities and differences in the molecular features found in the microbial and host TIR domains, which suggests mechanisms of bacterial mimicry of host signaling adaptor proteins, such as TIRAP.

Long-Term Enrichment on Cellulose or Xylan Causes Functional and Taxonomic Convergence of Microbial Communities from Anaerobic Digesters.

Cellulose and xylan are two major components of lignocellulosic biomass, which represents a potentially important energy source, as it is abundant and can be converted to methane by microbial action. However, it is recalcitrant to hydrolysis, and the establishment of a complete anaerobic digestion system requires a specific repertoire of microbial functions. In this study, we maintained 2-year enrichment cultures of anaerobic digestion sludge amended with cellulose or xylan to investigate whether a cellulose- or xylan-digesting microbial system could be assembled from sludge previously used to treat neither of them. While efficient methane-producing communities developed under mesophilic (35°C) incubation, they did not under thermophilic (55°C) conditions. Illumina amplicon sequencing results of the archaeal and bacterial 16S rRNA genes revealed that the mature cultures were much lower in richness than the inocula and were dominated by single archaeal (genus Methanobacterium) and bacterial (order Clostridiales) groups, although at finer taxonomic levels the bacteria were differentiated by substrates. Methanogenesis was primarily via the hydrogenotrophic pathway under all conditions, although the identity and growth requirements of syntrophic acetate-oxidizing bacteria were unclear. Incubation conditions (substrate and temperature) had a much greater effect than inoculum source in shaping the mature microbial community, although analysis based on unweighted UniFrac distance found that the inoculum still determined the pool from which microbes could be enriched. Overall, this study confirmed that anaerobic digestion sludge treating nonlignocellulosic material is a potential source of microbial cellulose- and xylan-digesting functions given appropriate enrichment conditions.

Pyrosequencing of mcrA and archaeal 16S rRNA genes reveals diversity and substrate preferences of methanogen communities in anaerobic digesters.

Methanogenic archaea play a key role in biogas-producing anaerobic digestion and yet remain poorly taxonomically characterized. This is in part due to the limitations of low-throughput Sanger sequencing of a single (16S rRNA) gene, which in the past may have undersampled methanogen diversity. In this study, archaeal communities from three sludge digesters in Hong Kong and one wastewater digester in China were examined using high-throughput pyrosequencing of the methyl coenzyme M reductase (mcrA) and 16S rRNA genes. Methanobacteriales, Methanomicrobiales, and Methanosarcinales were detected in each digester, indicating that both hydrogenotrophic and acetoclastic methanogenesis was occurring. Two sludge digesters had similar community structures, likely due to their similar design and feedstock. Taxonomic classification of the mcrA genes suggested that these digesters were dominated by acetoclastic methanogens, particularly Methanosarcinales, while the other digesters were dominated by hydrogenotrophic Methanomicrobiales. The proposed euryarchaeotal order Methanomassiliicoccales and the uncultured WSA2 group were detected with the 16S rRNA gene, and potential mcrA genes for these groups were identified. 16S rRNA gene sequencing also recovered several crenarchaeotal groups potentially involved in the initial anaerobic digestion processes. Overall, the two genes produced different taxonomic profiles for the digesters, while greater methanogen richness was detected using the mcrA gene, supporting the use of this functional gene as a complement to the 16S rRNA gene to better assess methanogen diversity. A significant positive correlation was detected between methane production and the abundance of mcrA transcripts in digesters treating sludge and wastewater samples, supporting the mcrA gene as a biomarker for methane yield.

Nuclear quantum effects in water exchange around lithium and fluoride ions.

We employ classical and ring polymer molecular dynamics simulations to study the effect of nuclear quantum fluctuations on the structure and the water exchange dynamics of aqueous solutions of lithium and fluoride ions. While we obtain reasonably good agreement with experimental data for solutions of lithium by augmenting the Coulombic interactions between the ion and the water molecules with a standard Lennard-Jones ion-oxygen potential, the same is not true for solutions of fluoride, for which we find that a potential with a softer repulsive wall gives much better agreement. A small degree of destabilization of the first hydration shell is found in quantum simulations of both ions when compared with classical simulations, with the shell becoming less sharply defined and the mean residence time of the water molecules in the shell decreasing. In line with these modest differences, we find that the mechanisms of the exchange processes are unaffected by quantization, so a classical description of these reactions gives qualitatively correct and quantitatively reasonable results. We also find that the quantum effects in solutions of lithium are larger than in solutions of fluoride. This is partly due to the stronger interaction of lithium with water molecules, partly due to the lighter mass of lithium and partly due to competing quantum effects in the hydration of fluoride, which are absent in the hydration of lithium.

Indoor-air microbiome in an urban subway network: diversity and dynamics.

Subway systems are indispensable for urban societies, but microbiological characteristics of subway aerosols are relatively unknown. Previous studies investigating microbial compositions in subways employed methodologies that underestimated the diversity of microbial exposure for commuters, with little focus on factors governing subway air microbiology, which may have public health implications. Here, a culture-independent approach unraveling the bacterial diversity within the urban subway network in Hong Kong is presented. Aerosol samples from multiple subway lines and outdoor locations were collected. Targeting the 16S rRNA gene V4 region, extensive taxonomic diversity was found, with the most common bacterial genera in the subway environment among those associated with skin. Overall, subway lines harbored different phylogenetic communities based on α- and ß-diversity comparisons, and closer inspection suggests that each community within a line is dependent on architectural characteristics, nearby outdoor microbiomes, and connectedness with other lines. Microbial diversities and assemblages also varied depending on the day sampled, as well as the time of day, and changes in microbial communities between peak and nonpeak commuting hours were attributed largely to increases in skin-associated genera in peak samples. Microbial diversities within the subway were influenced by temperature and relative humidity, while carbon dioxide levels showed a positive correlation with abundances of commuter-associated genera. This Hong Kong data set and communities from previous studies conducted in the United States formed distinct community clusters, indicating that additional work is required to unravel the mechanisms that shape subway microbiomes around the globe.

A qualitative investigation into care-leavers' experiences of accessing mental health support.

INTRODUCTION: People who grew up under the care of children's social services are a highly vulnerable group, with 50% of this population meeting the criteria for a mental health problem at any one time. Emerging evidence suggests that there is a disparity between the number of people who require support, and those that receive it, and that they face several barriers to accessing timely and effective mental health support. We have a limited understanding of how to support the mental health of this group as they 'age out' of children's social services, and the transition to independence, which occurs around the age of 18. We aimed to explore how care-leavers understand their experiences of help-seeking from formal mental health services. METHODS: We used qualitative interviews, and Interpretative Phenomenological Analysis with 9 care-experienced young people aged between 18 and 25 years old. This work was co-produced by a team of care-experienced adults, from the conception of the study to write-up. RESULTS: Qualitative analysis revealed several themes which centred around: (1) taking reluctant steps towards recovery, (2) challenges with being understood and the importance of gaining an understanding of yourself, (3) navigating trust and (4) the legacy of not having your mental health needs met. CONCLUSIONS: We identified several important implications for health and social care practice, across primary and secondary health care settings. This work highlights ways to better support this highly vulnerable group in accessing evidence-based mental health support, and how to maintain engagement.

Watch Me Play!: protocol for a feasibility study of a remotely delivered intervention to promote mental health resilience for children (ages 0-8) across UK early years and children's services.

BACKGROUND: Half of mental health problems are established by the age of 14 years and 75% by 24 years. Early intervention and prevention of mental ill health are therefore vitally important. However, increased demand over recent years has meant that access to child mental health services is often restricted to those in severest need. Watch Me Play! (WMP) is an early intervention designed to support caregiver attunement and attention to the child to promote social-emotional well-being and thereby mental health resilience. Originally developed in the context of a local authority mental health service for children in care, it is now also delivered online as a low intensity, scalable, preventative intervention. Although WMP shows promise and is already used in some services, we do not yet know whether it is effective. METHODS: A non-randomised single group feasibility study with embedded process evaluation. We propose to recruit up to 40 parents/carers of children aged 0-8 years who have been referred to early years and children's services in the UK. WMP involves a parent watching the child play and talking to their child about their play (or for babies, observing and following signals) for up to 20 min per session. Some sessions are facilitated by a trained practitioner who provides prompts where necessary, gives feedback, and discusses the child's play with the caregiver. Services will offer five facilitated sessions, and parents will be asked to do at least 10 additional sessions on their own with their child in a 5-week period. Feasibility outcomes examined are as follows: (i) recruitment, (ii) retention, (iii) adherence, (iv) fidelity of delivery, (v) barriers and facilitators of participation, (vi) intervention acceptability, (vii) description of usual care, and (viii) data collection procedures. Intervention mechanisms will be examined through qualitative interview data. Economic evaluation will be conducted estimating cost of the intervention and cost of service use for child and parents/carers quality-adjusted life years. DISCUSSION: This study will address feasibility questions associated with progression to a future randomised trial of WMP. TRIAL REGISTRATION: ISRCTN13644899 . Registered on 14th April 2023.

Family group conferencing for children and families: Evaluation of implementation, context and effectiveness (Family VOICE). Study protocol.

BACKGROUND: Family group conferences (FGCs) in child welfare bring immediate and wider family members together to decide on the best way to meet a child's needs. Unlike professionally led meetings, the aim is for decisions to be made by or with family members. Qualitative and mixed-method research with FGC participants tends to show positive experiences: most participants feel their voices are heard; FGCs facilitate family-driven solutions and closer relationships-within families and with social workers. Although there is existing literature on FGCs, there is a paucity of robust comparative UK evaluations, i.e., randomised controlled trials or quasi-experimental studies. Comparative studies internationally have focused on a narrow range of outcomes, not recognised the importance of context, and paid little attention to the quality of delivery. Some qualitative studies have considered process and context but there is scant measurement of these. The aims of this study are, firstly, to establish how FGCs improve outcomes for families and what factors vary their quality, and, secondly, to assess longer-term outcomes in terms of service use and associated costs. METHODS: Given the importance of process and context, evaluation informed by realist and complex systems approaches is needed. This multi-method evaluation includes a survey of FGC services in all UK local authorities (n = 212) to map service provision; co-design of programme theory and evaluation measures with family members who have experienced an FGC (n = 16-24) and practitioners (n = 16-24) in two sites; a prospective single-arm study of FGC variability and outcomes after six months; and comparison of service use and costs in FGC participants (n≥300 families) and a control group (n≥1000) after two years using a quasi-experiment. DISCUSSION: This is a pragmatic evaluation of an existing intervention, to identify what mechanisms and contexts influence effective process and longer-term outcomes. The study is registered with Research Registry (ref. 7432).