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Background Navigating the world of qualitative thematic analysis can be challenging. This is compounded by the fact that detailed descriptions of methods are often omitted from qualitative discussions. While qualitative research methodologies are now mature, there often remains a lack of fine detail in their description both at submitted peer reviewed article level and in textbooks. As one of research’s aims is to determine the relationship between knowledge and practice through the demonstration of rigour, more detailed descriptions of methods could prove useful. Rigour in quantitative research is often determined through detailed explanation allowing replication, but the ability to replicate is often not considered appropriate in qualitative research. However, a well described qualitative methodology could demonstrate and ensure the same effect. Methods This article details the codebook development which contributed to thematic analysis of qualitative data. This analysis formed part of a mixed methods multiphase design research project, with both qualitative and quantitative inquiry and involving the convergence of data and analyses. This design consisted of three distinct phases: quantitative, qualitative and implementation phases. Results and conclusions This article is aimed at researchers and doctoral students new to thematic analysis by describing a framework to assist their processes. The detailed description of the methods used supports attempts to utilise the thematic analysis process and to determine rigour to support the establishment of credibility. This process will assist practitioners to be confident that the knowledge and claims contained within research are transferable to their practice. The approach described within this article builds on, and enhances, current accepted models.

Background Logic models are commonly used in evaluations to represent the causal processes through which interventions produce outcomes, yet significant debate is currently taking place over whether they can describe complex interventions which adapt to context. This paper assesses the logic models used in healthcare research from a complexity perspective. A typology of existing logic models is proposed, as well as a formal methodology for deriving more flexible and dynamic logic models. Analysis Various logic model types were tested as part of an evaluation of a complex Patient Experience Toolkit (PET) intervention, developed and implemented through action research across six hospital wards/departments in the English NHS. Three dominant types of logic model were identified, each with certain strengths but ultimately unable to accurately capture the dynamics of PET. Hence, a fourth logic model type was developed to express how success hinges on the adaption of PET to its delivery settings. Aspects of the Promoting Action on Research Implementation in Health Services (PARIHS) model were incorporated into a traditional logic model structure to create a dynamic “type 4” logic model that can accommodate complex interventions taking on a different form in different settings. Conclusion Logic models can be used to model complex interventions that adapt to context but more flexible and dynamic models are required. An implication of this is that how logic models are used in healthcare research may have to change. Using logic models to forge consensus among stakeholders and/or provide precise guidance across different settings will be inappropriate in the case of complex interventions that adapt to context. Instead, logic models for complex interventions may be targeted at facilitators to enable them to prospectively assess the settings they will be working in and to develop context-sensitive facilitation strategies. Researchers should be clear as to why they are using a logic model and experiment with different models to ensure they have the correct type.

Countdown to 2015 tracks progress towards achievement of Millennium Development Goals (MDGs) 4 and 5, with particular emphasis on within-country inequalities. We assessed how inequalities in maternal, newborn, and child health interventions vary by intervention and country. We reanalysed data for 12 maternal, newborn, and child health interventions from national surveys done in 54 Countdown countries between Jan 1, 2000, and Dec 31, 2008. We calculated coverage indicators for interventions according to standard definitions, and stratified them by wealth quintiles on the basis of asset indices. We assessed inequalities with two summary indices for absolute inequality and two for relative inequality. Skilled birth attendant coverage was the least equitable intervention, according to all four summary indices, followed by four or more antenatal care visits. The most equitable intervention was early initation of breastfeeding. Chad, Nigeria, Somalia, Ethiopia, Laos, and Niger were the most inequitable countries for the interventions examined, followed by Madagascar, Pakistan, and India. The most equitable countries were Uzbekistan and Kyrgyzstan. Community-based interventions were more equally distributed than those delivered in health facilities. For all interventions, variability in coverage between countries was larger for the poorest than for the richest individuals. We noted substantial variations in coverage levels between interventions and countries. The most inequitable interventions should receive attention to ensure that all social groups are reached. Interventions delivered in health facilities need specific strategies to enable the countries' poorest individuals to be reached. The most inequitable countries need additional efforts to reduce the gap between the poorest individuals and those who are more affluent. Bill & Melinda Gates Foundation, Norad, The World Bank.

Background Decision making in health and social care requires robust syntheses of both quantitative and qualitative evidence. Meta-ethnography is a seven-phase methodology for synthesising qualitative studies. Developed in 1988 by sociologists in education Noblit and Hare, meta-ethnography has evolved since its inception; it is now widely used in healthcare research and is gaining popularity in education research. The aim of this article is to provide up-to-date, in-depth guidance on conducting the complex analytic synthesis phases 4 to 6 of meta-ethnography through analysis of the latest methodological evidence. Methods We report findings from a methodological systematic review conducted from 2015 to 2016. Fourteen databases and five other online resources were searched. Expansive searches were also conducted resulting in inclusion of 57 publications on meta-ethnography conduct and reporting from a range of academic disciplines published from 1988 to 2016. Results Current guidance on applying meta-ethnography originates from a small group of researchers using the methodology in a health context. We identified that researchers have operationalised the analysis and synthesis methods of meta-ethnography – determining how studies are related (phase 4), translating studies into one another (phase 5), synthesising translations (phase 6) and line of argument synthesis - to suit their own syntheses resulting in variation in methods and their application. Empirical research is required to compare the impact of different methods of translation and synthesis. Some methods are potentially better at preserving links with the context and meaning of primary studies, a key principle of meta-ethnography. A meta-ethnography can and should include reciprocal and refutational translation and line of argument synthesis, rather than only one of these, to maximise the impact of its outputs. Conclusion The current work is the first to articulate and differentiate the methodological variations and their application for different purposes and represents a significant advance in the understanding of the methodological application of meta-ethnography.

Introduction Successful implementation and embedding of new health care practices relies on co-ordinated, collective behaviour of individuals working within the constraints of health care settings. Normalization Process Theory (NPT) provides a theory of implementation that emphasises collective action in explaining, and shaping, the embedding of new practices. To extend the practical utility of NPT for improving implementation success, an instrument (NoMAD) was developed and validated. Methods Descriptive analysis and psychometric testing of an instrument developed by the authors, through an iterative process that included item generation, consensus methods, item appraisal, and cognitive testing. A 46 item questionnaire was tested in 6 sites implementing health related interventions, using paper and online completion. Participants were staff directly involved in working with the interventions. Descriptive analysis and consensus methods were used to remove redundancy, reducing the final tool to 23 items. Data were subject to confirmatory factor analysis which sought to confirm the theoretical structure within the sample. Results We obtained 831 completed questionnaires, an average response rate of 39% (range: 22–77%). Full completion of items was 50% ( n  = 413). The confirmatory factor analysis showed the model achieved acceptable fit (CFI = 0.95, TLI = 0.93, RMSEA = 0.08, SRMR = 0.03). Construct validity of the four theoretical constructs of NPT was supported, and internal consistency (Cronbach’s alpha) were as follows: Coherence (4 items, α = 0.71); Collective Action (7 items, α = 0.78); Cognitive Participation (4 items, α = 0.81); Reflexive Monitoring (5 items, α = 0.65). The normalisation scale overall, was highly reliable (20 items, α = 0.89). Conclusions The NoMAD instrument has good face validity, construct validity and internal consistency, for assessing staff perceptions of factors relevant to embedding interventions that change their work practices. Uses in evaluating and guiding implementation are proposed.

Background Survey research in healthcare is an important tool to collect information about healthcare delivery, service use and overall issues relating to quality of care. Unfortunately, physicians are often a group with low survey response rates and little research has looked at response rates among physician specialists. For these reasons, the purpose of this project was to explore survey response rates among physician specialists in a large metropolitan Canadian city. Methods As part of a larger project to look at physician payment plans, an online survey about medical billing practices was distributed to 904 physicians from various medical specialties. The primary method for physicians to complete the survey was via the Internet using a well-known and established survey company ( www.surveymonkey.com ). Multiple methods were used to encourage survey response such as individual personalized email invitations, multiple reminders, and a draw for three gift certificate prizes were used to increase response rate. Descriptive statistics were used to assess response rates and reasons for non-response. Results Overall survey response rate was 35.0%. Response rates varied by specialty: Neurology/neurosurgery (46.6%); internal medicine (42.9%); general surgery (29.6%); pediatrics (29.2%); and psychiatry (27.1%). Non-respondents listed lack of time/survey burden as the main reason for not responding to our survey. Conclusions Our survey results provide a look into the challenges of collecting healthcare research where response rates to surveys are often low. The findings presented here should help researchers in planning future survey based studies. Findings from this study and others suggest smaller monetary incentives for each individual may be a more appropriate way to increase response rates.

Discrete-choice experiments (DCEs) have become a commonly used instrument in health economics and patient-preference analysis, addressing a wide range of policy questions. An important question when setting up a DCE is the size of the sample needed to answer the research question of interest. Although theory exists as to the calculation of sample size requirements for stated choice data, it does not address the issue of minimum sample size requirements in terms of the statistical power of hypothesis tests on the estimated coefficients. The purpose of this paper is threefold: (1) to provide insight into whether and how researchers have dealt with sample size calculations for healthcare-related DCE studies; (2) to introduce and explain the required sample size for parameter estimates in DCEs; and (3) to provide a step-by-step guide for the calculation of the minimum sample size requirements for DCEs in health care.

Background An increasing number of qualitative evidence syntheses papers are found in health care literature. Many of these syntheses use a strictly exhaustive search strategy to collect articles, mirroring the standard template developed by major review organizations such as the Cochrane and Campbell Collaboration. The hegemonic idea behind it is that non-comprehensive samples in systematic reviews may introduce selection bias. However, exhaustive sampling in a qualitative evidence synthesis has been questioned, and a more purposeful way of sampling papers has been proposed as an alternative, although there is a lack of transparency on how these purposeful sampling strategies might be applied to a qualitative evidence synthesis. We discuss in our paper why and how we used purposeful sampling in a qualitative evidence synthesis about ‘sexual adjustment to a cancer trajectory’, by giving a worked example. Methods We have chosen a mixed purposeful sampling, combining three different strategies that we considered the most consistent with our research purpose: intensity sampling, maximum variation sampling and confirming/disconfirming case sampling. Results The concept of purposeful sampling on the meta-level could not readily been borrowed from the logic applied in basic research projects. It also demands a considerable amount of flexibility, and is labour-intensive, which goes against the argument of many authors that using purposeful sampling provides a pragmatic solution or a short cut for researchers, compared with exhaustive sampling. Opportunities of purposeful sampling were the possible inclusion of new perspectives to the line-of-argument and the enhancement of the theoretical diversity of the papers being included, which could make the results more conceptually aligned with the synthesis purpose. Conclusions This paper helps researchers to make decisions related to purposeful sampling in a more systematic and transparent way. Future research could confirm or disconfirm the hypothesis of conceptual enhancement by comparing the findings of a purposefully sampled qualitative evidence synthesis with those drawing on an exhaustive sample of the literature.

Background Greater interest is being shown in participatory approaches, especially in research on interventions that concern children and young people’s health and well-being. Although participatory approaches have user involvement in common, they differ in terms of the explicit guidance on how to actually involve and engage children and young people in health research. The aim of this scoping review was to systematically map recent research involving children and young people in the development of interventions targeting issues of health and well-being. Methods An interpretative scoping literature review based on: a scientific literature search in (health and social science) databases, reference lists, a manual search in key journals and contact with existing networks was conducted. A total of 4458 references were identified through the literature search, of which 41 studies published between 2000 and 2017 were included in the review. The target population was children and young people under 25 years old. Level of participation was categorized according to Shier’s Pathways to Participation Model. Results The review showed that participatory approaches were most often used in the development of interventions in school settings and in community and healthcare settings and on issues concerning support in lifestyle or in managing illness or disease. The level of participation varied from children and young people taking part just as active informants, through stages of greater participation both in quantitative and qualitative terms, to children and young people becoming an active agent involved as a co-researcher where the research process was shaped by views of a higher level of mutuality. Most of the studies were categorised at a medium level and only three studies were judged to involve the children and young people at the highest level. Conclusions This scoping review showed that work remains in enabling children and young people to influence the development of interventions targeting health and well-being. In relation to level of sustainability in the interventions, it is relevant that goals, strategies and processes are formulated by those who can gain from the interventions. Participatory approaches aiming for a higher level of participation where children and young people work together with the researchers in partnerships are thus warranted.

Considerable progress has been made in reducing maternal, newborn, and child mortality worldwide, but many more deaths could be prevented if effective interventions were available to all who could benefit from them. Timely, high-quality measurements of intervention coverage--the proportion of a population in need of a health intervention that actually receives it--are essential to support sound decisions about progress and investments in women's and children's health. The PLOS Medicine \"Measuring Coverage in MNCH\" Collection of research studies and reviews presents systematic assessments of the validity of health intervention coverage measurement based on household surveys, the primary method for estimating population-level intervention coverage in low- and middle-income countries. In this overview of the Collection, we discuss how and why some of the indicators now being used to track intervention coverage may not provide fully reliable coverage measurements, and how a better understanding of the systematic and random error inherent in these coverage indicators can help in their interpretation and use. We draw together strategies proposed across the Collection for improving coverage measurement, and recommend continued support for high-quality household surveys at national and sub-national levels, supplemented by surveys with lighter tools that can be implemented every 1-2 years and by complementary health-facility-based assessments of service quality. Finally, we stress the importance of learning more about coverage measurement to strengthen the foundation for assessing and improving the progress of maternal, newborn, and child health programs.