BackgroundThe large and growing number of published studies, and their increasing rate of publication, makes the task of identifying relevant studies in an unbiased way for inclusion in systematic reviews both complex and time consuming. Text mining has been offered as a potential solution: through automating some of the screening process, reviewer time can be saved. The evidence base around the use of text mining for screening has not yet been pulled together systematically; this systematic review fills that research gap. Focusing mainly on non-technical issues, the review aims to increase awareness of the potential of these technologies and promote further collaborative research between the computer science and systematic review communities.MethodsFive research questions led our review: what is the state of the evidence base; how has workload reduction been evaluated; what are the purposes of semi-automation and how effective are they; how have key contextual problems of applying text mining to the systematic review field been addressed; and what challenges to implementation have emerged?We answered these questions using standard systematic review methods: systematic and exhaustive searching, quality-assured data extraction and a narrative synthesis to synthesise findings.ResultsThe evidence base is active and diverse; there is almost no replication between studies or collaboration between research teams and, whilst it is difficult to establish any overall conclusions about best approaches, it is clear that efficiencies and reductions in workload are potentially achievable.On the whole, most suggested that a saving in workload of between 30% and 70% might be possible, though sometimes the saving in workload is accompanied by the loss of 5% of relevant studies (i.e. a 95% recall).ConclusionsUsing text mining to prioritise the order in which items are screened should be considered safe and ready for use in ‘live’ reviews. The use of text mining as a ‘second screener’ may also be used cautiously. The use of text mining to eliminate studies automatically should be considered promising, but not yet fully proven. In highly technical/clinical areas, it may be used with a high degree of confidence; but more developmental and evaluative work is needed in other disciplines.Electronic supplementary materialThe online version of this article (doi:10.1186/2046-4053-4-5) contains supplementary material, which is available to authorized users.

BackgroundCommunity engagement has been advanced as a promising way of improving health and reducing health inequalities; however, the approach is not yet supported by a strong evidence base.ObjectivesTo undertake a multimethod systematic review which builds on the evidence that underpins the current UK guidance on community engagement; to identify theoretical models underpinning community engagement; to explore mechanisms and contexts through which communities are engaged; to identify community engagement approaches that are effective in reducing health inequalities, under what circumstances and for whom; and to determine the processes and costs associated with their implementation.Data sourcesDatabases including the Cochrane Database of Systematic Reviews (CDSR), The Campbell Library, the Database of Abstracts of Reviews of Effects (DARE), the Health Technology Assessment (HTA) database, the NHS Economic Evaluation Database (NHS EED) and EPPI-Centre’s Trials Register of Promoting Health Interventions (TRoPHI) and Database of Promoting Health Effectiveness Reviews (DoPHER) were searched from 1990 to August 2011 for systematic reviews and primary studies. Trials evaluating community engagement interventions reporting health outcomes were included.Review methodsStudy eligibility criteria: published after 1990; outcome, economic, or process evaluation; intervention relevant to community engagement; written in English; measured and reported health or community outcomes, or presents cost, resource, or implementation data characterises study populations or reports differential impacts in terms of social determinants of health; conducted in an Organisation for Economic Co-operation and Development (OECD) country. Study appraisal: risk of bias for outcome evaluations; assessment of validity and relevance for process evaluations; comparison against an economic evaluation checklist for economic evaluations. Synthesis methods: four synthesis approaches were adopted for the different evidence types: theoretical, quantitative, process, and economic evidence.ResultsThe theoretical synthesis identified key models of community engagement that are underpinned by different theories of changes. Results from 131 studies included in a meta-analysis indicate that there is solid evidence that community engagement interventions have a positive impact on health behaviours, health consequences, self-efficacy and perceived social support outcomes, across various conditions. There is insufficient evidence – particularly for long-term outcomes and indirect beneficiaries – to determine whether one particular model of community engagement is likely to be more effective than any other. There are also insufficient data to test the effects on health inequalities, although there is some evidence to suggest that interventions that improve social inequalities (as measured by social support) also improve health behaviours. There is weak evidence from the effectiveness and process evaluations that certain implementation factors may affect intervention success. From the economic analysis, there is weak but inconsistent evidence that community engagement interventions are cost-effective. By combining findings across the syntheses, we produced a new conceptual framework.LimitationsDifferences in the populations, intervention approaches and health outcomes made it difficult to pinpoint specific strategies for intervention effectiveness. The syntheses of process and economic evidence were limited by the small (generally not rigorous) evidence base.ConclusionsCommunity engagement interventions are effective across a wide range of contexts and using a variety of mechanisms. Public health initiatives should incorporate community engagement into intervention design. Evaluations should place greater emphasis on long-term outcomes, outcomes for indirect beneficiaries, process evaluation, and reporting costs and resources data. The theories of change identified and the newly developed conceptual framework are useful tools for researchers and practitioners. We identified trends in the evidence that could provide useful directions for future intervention design and evaluation.FundingThe National Institute for Health Research Public Health Research programme.

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BackgroundInequalities 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.MethodsWe 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.ResultsOf 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.ConclusionsThere 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12889-015-1352-y) contains supplementary material, which is available to authorized users.

Background Tobacco smoking in pregnancy remains one of the few preventable factors associated with complications in pregnancy, stillbirth, low birthweight and preterm birth and has serious long-term implications for women and babies. Smoking in pregnancy is decreasing in high-income countries, but is strongly associated with poverty and increasing in low- to middle-income countries. Objectives To assess the effects of smoking cessation interventions during pregnancy on smoking behaviour and perinatal health outcomes. Search methods In this fifth update, we searched the Cochrane Pregnancy and Childbirth Group’s Trials Register (1 March 2013), checked reference lists of retrieved studies and contacted trial authors to locate additional unpublished data. Selection criteria Randomised controlled trials, cluster-randomised trials, randomised cross-over trials, and quasi-randomised controlled trials (with allocation by maternal birth date or hospital record number) of psychosocial smoking cessation interventions during pregnancy. Data collection and analysis Two review authors independently assessed trials for inclusion and trial quality, and extracted data. Direct comparisons were conducted in RevMan, and subgroup analyses and sensitivity analysis were conducted in SPSS. Main results Eighty-six trials were included in this updated review, with 77 trials (involving over 29,000 women) providing data on smoking abstinence in late pregnancy. In separate comparisons, counselling interventions demonstrated a significant effect compared with usual care (27 studies; average risk ratio (RR) 1.44, 95% confidence interval (CI) 1.19 to 1.75), and a borderline effect compared with less intensive interventions (16 studies; average RR 1.35, 95% CI 1.00 to 1.82). However, a significant effect was only seen in subsets where counselling was provided in conjunction with other strategies. It was unclear whether any type of counselling strategy is more effective than others (one study; RR 1.15, 95% CI 0.86 to 1.53). In studies comparing counselling and usual care (the largest comparison), it was unclear whether interventions prevented smoking relapse among women who had stopped smoking spontaneously in early pregnancy (eight studies; average RR 1.06, 95% CI 0.93 to 1.21). However, a clear effect was seen in smoking abstinence at zero to five months postpartum (10 studies; average RR 1.76, 95% CI 1.05 to 2.95), a borderline effect at six to 11 months (six studies; average RR 1.33, 95% CI 1.00 to 1.77), and a significant effect at 12 to 17 months (two studies, average RR 2.20, 95% CI 1.23 to 3.96), but not in the longer term. In other comparisons, the effect was not significantly different from the null effect for most secondary outcomes, but sample sizes were small. Incentive-based interventions had the largest effect size compared with a less intensive intervention (one study; RR 3.64, 95% CI 1.84 to 7.23) and an alternative intervention (one study; RR 4.05, 95% CI 1.48 to 11.11). Feedback interventions demonstrated a significa...

BackgroundBehaviour change is key to addressing both the challenges facing human health and wellbeing and to promoting the uptake of research findings in health policy and practice. We need to make better use of the vast amount of accumulating evidence from behaviour change intervention (BCI) evaluations and promote the uptake of that evidence into a wide range of contexts. The scale and complexity of the task of synthesising and interpreting this evidence, and increasing evidence timeliness and accessibility, will require increased computer support.The Human Behaviour-Change Project (HBCP) will use Artificial Intelligence and Machine Learning to (i) develop and evaluate a ‘Knowledge System’ that automatically extracts, synthesises and interprets findings from BCI evaluation reports to generate new insights about behaviour change and improve prediction of intervention effectiveness and (ii) allow users, such as practitioners, policy makers and researchers, to easily and efficiently query the system to get answers to variants of the question ‘What works, compared with what, how well, with what exposure, with what behaviours (for how long), for whom, in what settings and why?’.MethodsThe HBCP will: a) develop an ontology of BCI evaluations and their reports linking effect sizes for given target behaviours with intervention content and delivery and mechanisms of action, as moderated by exposure, populations and settings; b) develop and train an automated feature extraction system to annotate BCI evaluation reports using this ontology; c) develop and train machine learning and reasoning algorithms to use the annotated BCI evaluation reports to predict effect sizes for particular combinations of behaviours, interventions, populations and settings; d) build user and machine interfaces for interrogating and updating the knowledge base; and e) evaluate all the above in terms of performance and utility.DiscussionThe HBCP aims to revolutionise our ability to synthesise, interpret and deliver evidence on behaviour change interventions that is up-to-date and tailored to user need and context. This will enhance the usefulness, and support the implementation of, that evidence.Electronic supplementary materialThe online version of this article (10.1186/s13012-017-0641-5) contains supplementary material, which is available to authorized users.

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BackgroundGovernment policy increasingly supports engaging communities to promote health. It is critical to consider whether such strategies are effective, for whom, and under what circumstances. However, ‘community engagement’ is defined in diverse ways and employed for different reasons. Considering the theory and context we developed a conceptual framework which informs understanding about what makes an effective (or ineffective) community engagement intervention.MethodsWe conducted a systematic review of community engagement in public health interventions using: stakeholder involvement; searching, screening, appraisal and coding of research literature; and iterative thematic syntheses and meta-analysis. A conceptual framework of community engagement was refined, following interactions between the framework and each review stage.ResultsFrom 335 included reports, three products emerged: (1) two strong theoretical ‘meta-narratives’: one, concerning the theory and practice of empowerment/engagement as an independent objective; and a more utilitarian perspective optimally configuring health services to achieve defined outcomes. These informed (2) models that were operationalized in subsequent meta-analysis. Both refined (3) the final conceptual framework. This identified multiple dimensions by which community engagement interventions may differ. Diverse combinations of intervention purpose, theory and implementation were noted, including: ways of defining communities and health needs; initial motivations for community engagement; types of participation; conditions and actions necessary for engagement; and potential issues influencing impact. Some dimensions consistently co-occurred, leading to three overarching models of effective engagement which either: utilised peer-led delivery; employed varying degrees of collaboration between communities and health services; or built on empowerment philosophies.ConclusionsOur conceptual framework and models are useful tools for considering appropriate and effective approaches to community engagement. These should be tested and adapted to facilitate intervention design and evaluation. Using this framework may disentangle the relative effectiveness of different models of community engagement, promoting effective, sustainable and appropriate initiatives.