The 'active ingredients' for successful community engagement with disadvantaged expectant and new mothers: a qualitative comparative analysis

AIMS: To explore which conditions of community engagement are implicated in effective interventions targeting disadvantaged pregnant women and new mothers. BACKGROUND: Adaptive experiences during pregnancy and the early years are key to reducing health inequalities in women and children worldwide. Public health nurses, health visitors and community midwives are well placed to address such disadvantage, often using community engagement strategies. Such interventions are complex; however, and we need to better understand which aspects of community engagement are aligned with effectiveness. DESIGN: Qualitative comparative analysis conducted in 2013, of trials data included in a recently published systematic review. METHODS: Two reviewers agreed on relevant conditions from 24 maternity or early years intervention studies examining four models of community engagement. Effect size estimates were converted into 'fuzzy' effectiveness categories and truth tables were constructed. Using fsQCA software, Boolean minimization identified solution sets. Random effects multiple regression and fsQCA were conducted to rule out risk of methodological bias. RESULTS/FINDINGS: Studies focused on antenatal, immunization, breastfeeding and early professional intervention outcomes. Peer delivery (consistency 0·83; unique coverage 0·63); and mother-professional collaboration (consistency 0·833; unique coverage 0·21) were moderately aligned with effective interventions. Community-identified health need plus consultation/collaboration in intervention design and leading on delivery were weakly aligned with 'not effective' interventions (consistency 0·78; unique coverage 0·29). CONCLUSIONS: For disadvantaged new and expectant mothers, peer or collaborative delivery models could be used in interventions. A need exists to design and test community engagement interventions in other areas of maternity and early years care and to further evaluate models of empowerment

The effectiveness of community engagement in public health interventions for disadvantaged groups: a meta-analysis

BACKGROUND: Inequalities in health are acknowledged in many developed countries, whereby disadvantaged groups systematically suffer from worse health outcomes such as lower life expectancy than non-disadvantaged groups. Engaging members of disadvantaged communities in public health initiatives has been suggested as a way to reduce health inequities. This systematic review was conducted to evaluate the effectiveness of public health interventions that engage the community on a range of health outcomes across diverse health issues. METHODS: We searched the following sources for systematic reviews of public health interventions: Cochrane CDSR and CENTRAL, Campbell Library, DARE, NIHR HTA programme website, HTA database, and DoPHER. Through the identified reviews, we collated a database of primary studies that appeared to be relevant, and screened the full-text documents of those primary studies against our inclusion criteria. In parallel, we searched the NHS EED and TRoPHI databases for additional primary studies. For the purposes of these analyses, study design was limited to randomised and non-randomised controlled trials. Only interventions conducted in OECD countries and published since 1990 were included. We conducted a random effects meta-analysis of health behaviour, health consequences, self-efficacy, and social support outcomes, and a narrative summary of community outcomes. We tested a range of moderator variables, with a particular emphasis on the model of community engagement used as a potential moderator of intervention effectiveness. RESULTS: Of the 9,467 primary studies scanned, we identified 131 for inclusion in the meta-analysis. The overall effect size for health behaviour outcomes is d = .33 (95% CI .26, .40). The interventions were also effective in increasing health consequences (d = .16, 95% CI .06, .27); health behaviour self-efficacy (d = .41, 95% CI .16, .65) and perceived social support (d = .41, 95% CI .23, .65). Although the type of community engagement was not a significant moderator of effect, we identified some trends across studies. CONCLUSIONS: There is solid evidence that community engagement interventions have a positive impact on a range of health outcomes across various conditions. There is insufficient evidence to determine whether one particular model of community engagement is more effective than any other

Meta-analysis, complexity, and heterogeneity: a qualitative interview study of researchers’ methodological values and practices

Background Complex or heterogeneous data pose challenges for systematic review and meta-analysis. In recent years, a number of new methods have been developed to meet these challenges. This qualitative interview study aimed to understand researchers’ understanding of complexity and heterogeneity and the factors which may influence the choices researchers make in synthesising complex data. Methods We conducted interviews with a purposive sample of researchers (N = 19) working in systematic review or meta-analysis across a range of disciplines. We analysed data thematically using a framework approach. Results Participants reported using a broader range of methods and data types in complex reviews than in traditional reviews. A range of techniques are used to explore heterogeneity, but there is some debate about their validity, particularly when applied post hoc. Conclusions Technical considerations of how to synthesise complex evidence cannot be isolated from questions of the goals and contexts of research. However, decisions about how to analyse data appear to be made in a largely informal way, drawing on tacit expertise, and their relation to these broader questions remains unclear

‘Missing out’: Reflections on the positioning of ethnographic research within an evaluative framing

Contemporary approaches to evaluating ‘complex’ social and health interventions are opening up spaces for methodologies attuned to examining contextual complexities, such as ethnography. Yet the alignment of the two agendas – evaluative and ethnographic – is not necessarily comfortable in practice. I reflect on experiences of conducting ethnographic research alongside a public health evaluation of a community-based initiative in the UK, using the lens of ‘missing out’ to examine intersections between my own ethnographic concerns and those of the communities under study. I examine potential opportunities posed by the discomfort of ‘missing out’, particularly for identifying the processes and spaces of inclusion and exclusion that contributed both to my ethnographic experiences and to the realities of the communities engaging with the initiative. This reveals productive possibilities for a focus on ‘missing out’ as a form of relating for evaluations of the impacts of such initiatives on health and social inequalities

Creating a database of internet-based clinical trials to support a public-led research programme: A descriptive analysis

Background: Online trials are rapidly growing in number, offering potential benefits but also methodological, ethical and social challenges. The International Network for Knowledge on Well-being (ThinkWell™) aims to increase public and patient participation in the prioritisation, design and conduct of research through the use of technologies. Objective: We aim to provide a baseline understanding of the online trial environment, determining how many trials have used internet-based technologies; how they have been used; and how use has developed over time. Methods: We searched a range of bibliographic databases to March 2015, with no date limits, supplemented by citation searching and references provided by experts in the field. Results were screened against inclusion and exclusion criteria, and included studies mapped against a number of key dimensions, with key themes developed iteratively throughout the process. Results: We identified 1992 internet-based trials to March 2015. The number of reported studies increased substantially over the study timeframe. The largest number of trials were conducted in the USA (49.7%), followed by The Netherlands (10.2%); Australia (8.5%); the United Kingdom (5.8%); Sweden (4.6%); Canada (4%); and Germany (2.6%). South Korea (1.5%) has the highest number of reported trials for other continents. There is a predominance of interventions addressing core public health challenges including obesity (8.6%), smoking cessation (5.9%), alcohol abuse (7.7%) and physical activity (10.2%); in mental health issues such as depression (10.9%) and anxiety (5.6%); and conditions where self-management (16.6%) or monitoring (8.1%) is a major feature of care. Conclusions: The results confirm an increase in the use of the internet in trials. Key themes have emerged from the analysis and further research will be undertaken in order to investigate how the data can be used to improve trial design and recruitment, and to build an open access resource to support the public-led research agenda

Discrimination of dark matter models in future experiments

Phenomenological aspects of simple dark matter models are studied. We discuss ways to discriminate the dark matter models in future experiments. We find that the measurements of the branching fraction of the Higgs boson into two photons and the electric dipole moment of the electron as well as the direct detection experiments are quite useful in discriminating particle models of dark matter. We also discuss the prospects of finding new particles in dark sector at the LHC/ILC.Comment: 39 pages, 20 figures; v3 a typo in the Appendix A.1 is correcte

Prioritising references for systematic reviews with RobotAnalyst: A user study

Screening references is a time-consuming step necessary for systematic reviews and guideline development. Previous studies have shown that human effort can be reduced by using machine learning software to prioritise large reference collections such that most of the relevant references are identified before screening is completed. We describe and evaluate RobotAnalyst, a Web-based software system that combines text-mining and machine learning algorithms for organising references by their content and actively prioritising them based on a relevancy classification model trained and updated throughout the process. We report an evaluation over 22 reference collections (most are related to public health topics) screened using RobotAnalyst with a total of 43 610 abstract-level decisions. The number of references that needed to be screened to identify 95% of the abstract-level inclusions for the evidence review was reduced on 19 of the 22 collections. Significant gains over random sampling were achieved for all reviews conducted with active prioritisation, as compared with only two of five when prioritisation was not used. RobotAnalyst's descriptive clustering and topic modelling functionalities were also evaluated by public health analysts. Descriptive clustering provided more coherent organisation than topic modelling, and the content of the clusters was apparent to the users across a varying number of clusters. This is the first large-scale study using technology-assisted screening to perform new reviews, and the positive results provide empirical evidence that RobotAnalyst can accelerate the identification of relevant studies. The results also highlight the issue of user complacency and the need for a stopping criterion to realise the work savings

Conceptual and statistical analysis of complex interventions in the presence of confounding variables: An example from public health

Background: Meta-analyses of complex interventions are challenging because causality operates through multiple paths and confounding variables can be difficult to distinguish. Objectives: To meta-analyse public health interventions that engage members of the community in their conception, design, or delivery. To disentangle intervention complexity by analysing according to their theories of change. Study selection criteria: Published after 1990; outcome or process evaluation; community engagement intervention; written in English; reported health or community outcomes; study populations or differential impacts reported according to social determinants of health. Analysis: Intervention complexity was examined by conceptualising, operationalising, and mapping their theories of change; and through random effects subgroup analyses. Main results: 131 studies were included in the synthesis. Three main theories of change were identified, which were useful in describing trends in intervention effectiveness. Statistically significant between-group differences were not detected, since there were likely to have been too many confounding variables. Conclusions: Intervention complexity in systematic reviews can be addressed through examining theories of change and trends in effect size estimates. Such complexity appears to defy current meta-analytical methods when confounding variables undermine analysis of variance