Exploring the feasibility of theory synthesis: a worked example in the field of health related risk-taking.
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ABSTRACT:
SUBMITTER: Pound P
PROVIDER: S-EPMC4292939 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundA variety of different approaches to the synthesis of qualitative data are advocated in the literature. The aim of this paper is to describe the application of a pragmatic method of qualitative evidence synthesis and the lessons learned from adopting this "best fit" framework synthesis approach.MethodsAn evaluation of framework synthesis as an approach to the qualitative systematic review of evidence exploring the views of adults to the taking of potential agents within the context of the primary prevention of colorectal cancer.ResultsTwenty papers from North America, Australia, the UK and Europe met the criteria for inclusion. Fourteen themes were identified a priori from a related, existing conceptual model identified in the literature, which were then used to code the extracted data. Further analysis resulted in the generation of a more sophisticated model with additional themes. The synthesis required a combination of secondary framework and thematic analysis approaches and was conducted within a health technology assessment timeframe.ConclusionThe novel and pragmatic "best fit" approach to framework synthesis developed and described here was found to be fit for purpose. Future research should seek to test further this approach to qualitative data synthesis.
| S-EPMC3068987 | biostudies-literature
Project description:BACKGROUND:Qualitative methodologies are increasingly popular in medical research. Grounded theory is the methodology most-often cited by authors of qualitative studies in medicine, but it has been suggested that many 'grounded theory' studies are not concordant with the methodology. In this paper we provide a worked example of a grounded theory project. Our aim is to provide a model for practice, to connect medical researchers with a useful methodology, and to increase the quality of 'grounded theory' research published in the medical literature. METHODS:We documented a worked example of using grounded theory methodology in practice. RESULTS:We describe our sampling, data collection, data analysis and interpretation. We explain how these steps were consistent with grounded theory methodology, and show how they related to one another. Grounded theory methodology assisted us to develop a detailed model of the process of adapting preventive protocols into dental practice, and to analyse variation in this process in different dental practices. CONCLUSIONS:By employing grounded theory methodology rigorously, medical researchers can better design and justify their methods, and produce high-quality findings that will be more useful to patients, professionals and the research community.
| S-EPMC3184112 | biostudies-literature
Project description:BACKGROUND:In a qualitative evidence synthesis, too much data due to a large number of studies can undermine our ability to perform a thorough analysis. Purposive sampling of primary studies for inclusion in the synthesis is one way of achieving a manageable amount of data. The objective of this article is to describe the development and application of a sampling framework for a qualitative evidence synthesis on vaccination communication. METHODS:We developed and applied a three-step framework to sample studies from among those eligible for inclusion in our synthesis. We aimed to prioritise studies that were from a range of settings, were as relevant as possible to the review, and had rich data. We extracted information from each study about country and study setting, vaccine, data richness, and study objectives and applied the following sampling framework: 1. Studies conducted in low and middle income settings 2. Studies scoring four or more on a 5-point scale of data richness 3. Studies where the study objectives closely matched our synthesis objectives RESULTS: We assessed 79 studies as eligible for inclusion in the synthesis and sampled 38 of these. First, we sampled all nine studies that were from low and middle-income countries. These studies contributed to the least number of findings. We then sampled an additional 24 studies that scored high for data richness. These studies contributed to a larger number of findings. Finally, we sampled an additional five studies that most closely matched our synthesis objectives. These contributed to a large number of findings. CONCLUSIONS:Our approach to purposive sampling helped ensure that we included studies representing a wide geographic spread, rich data and a focus that closely resembled our synthesis objective. It is possible that we may have overlooked primary studies that did not meet our sampling criteria but would have contributed to the synthesis. For example, two studies on migration and access to health services did not meet the sampling criteria but might have contributed to strengthening at least one finding. We need methods to cross-check for under-represented themes.
| S-EPMC6357413 | biostudies-literature
Project description:The concept of an optimum yield at intermediate levels of fishing (the so called maximum sustainable yield or MSY) has been with us since the 1930s and is now enshrined in legislation as a key objective of fisheries management. The concept seems intuitively reasonable and is readily applicable to a single stock treated in isolation and assuming a constant environment. However, translating this concept into a mixed and multispecies fishery, where there are complex trade-offs between fleets and stocks and in general no simple optimum solution, has been problematic. Here I introduce a framework for thinking about multispecies MSY in terms of an integrated risk of stock depletion and expected long-term yield. Within this framework I consider the performance of a set of simple harvest control rules based upon a single-limit fishing mortality rate (F) which is common to all stocks and a target biomass which is a set fraction of a stock's virgin biomass. Using a multispecies management strategy evaluation, I compare expected outcomes for a set of these harvest control rules with alternative scenarios, in which each stock has its own F based on the assessment process. I find that the simple framework can produce outcomes that are similar to those from the more sophisticated estimates of F. I therefore conclude that achieving multispecies MSY may depend more upon setting reasonable biomass targets and faithfully applying a harvest control rule approach rather than determining the best possible Fs for each stock.
| S-EPMC6900025 | biostudies-literature
Project description:In contexts where healthcare regulation is weak and levels of uncertainty high, how do patients decide whom and what to trust? In this paper, we explore the potential for using Signalling Theory (ST, a form of Behavioural Game Theory) to investigate health-related trust problems under conditions of uncertainty, using the empirical example of 'herbal clinics' in Ghana and Tanzania. Qualitative, ethnographic fieldwork was conducted over an eight-month period (2015-2016) in eight herbal clinics in Ghana and ten in Tanzania, including semi-structured interviews with herbalists (N = 18) and patients (N = 68), plus detailed ethnographic observations and twenty additional key informant interviews. The data were used to explore four ST-derived predictions, relating to herbalists' strategic communication ('signalling') of their trustworthiness to patients, and patients' interpretation of those signals. Signalling Theory is shown to provide a useful analytical framework, allowing us to go beyond the primary trust problem addressed by other researchers - cataloguing observable indicators of trustworthiness - and providing tools for tackling the trickier secondary trust problem, where the trustworthiness of those indicators must be ascertained. Signalling Theory also enables a basis for comparative work between different empirical contexts that share the underlying condition of uncertainty.
| S-EPMC5559643 | biostudies-literature
Project description:Mobile health, or 'mHealth', is the application of mobile devices, their components and related technologies to healthcare. It is already improving patients' access to treatment and advice. Now, in combination with internet-connected diagnostic devices, it offers novel ways to diagnose, track and control infectious diseases and to improve the efficiency of the health system. Here we examine the promise of these technologies and discuss the challenges in realizing their potential to increase patients' access to testing, aid in their treatment and improve the capability of public health authorities to monitor outbreaks, implement response strategies and assess the impact of interventions across the world.
| S-EPMC6776470 | biostudies-literature
Project description:The parametric g-formula can be used to estimate the effect of a policy, intervention, or treatment. Unlike standard regression approaches, the parametric g-formula can be used to adjust for time-varying confounders that are affected by prior exposures. To date, there are few published examples in which the method has been applied.We provide a simple introduction to the parametric g-formula and illustrate its application in an analysis of a small cohort study of bone marrow transplant patients in which the effect of treatment on mortality is subject to time-varying confounding.Standard regression adjustment yields a biased estimate of the effect of treatment on mortality relative to the estimate obtained by the g-formula.The g-formula allows estimation of a relevant parameter for public health officials: the change in the hazard of mortality under a hypothetical intervention, such as reduction of exposure to a harmful agent or introduction of a beneficial new treatment. We present a simple approach to implement the parametric g-formula that is sufficiently general to allow easy adaptation to many settings of public health relevance.
| S-EPMC4310506 | biostudies-literature
Project description:We discuss different methods of sample size calculation for two independent means, aiming to provide insight into the calculation of sample size at the design stage of a parallel two-arm randomised controlled trial (RCT). We compare different methods for sample size calculation, using published results from a previous RCT. We use variances and correlation coefficients to compare sample sizes using different methods, including 1. The choice of the primary outcome measure: post-intervention score vs. change from baseline score. 2. The choice of statistical methods: t-test without using correlation coefficients vs. analysis of covariance (ANCOVA). We show that the required sample size will depend on whether the outcome measure is the post-intervention score, or the change from baseline score, with or without baseline score included as a covariate. We show that certain assumptions have to be met when using simplified sample size equations, and discuss their implications in sample size calculation when planning an RCT. We strongly recommend publishing the crucial result "mean change (SE, standard error)" in a study paper, because it allows (i) the calculation of the variance of the change score in each arm, and (ii) to pool the variances from both arms. It also enables us to calculate the correlation coefficient in each arm. This subsequently allows us to calculate sample size using change score as the outcome measure. We use simulation to demonstrate how sample sizes by different methods are influenced by the strength of the correlation.
| S-EPMC6297128 | biostudies-other
Project description:IntroductionSyndromic surveillance aims at augmenting traditional public health surveillance with timely information. To gain a head start, it mainly analyses existing data such as from web searches or patient records. Despite the setup of many syndromic surveillance systems, there is still much doubt about the benefit of the approach. There are diverse interactions between performance indicators such as timeliness and various system characteristics. This makes the performance assessment of syndromic surveillance systems a complex endeavour. We assessed if the comparison of several syndromic surveillance systems through Qualitative Comparative Analysis helps to evaluate performance and identify key success factors.Materials and methodsWe compiled case-based, mixed data on performance and characteristics of 19 syndromic surveillance systems in Europe from scientific and grey literature and from site visits. We identified success factors by applying crisp-set Qualitative Comparative Analysis. We focused on two main areas of syndromic surveillance application: seasonal influenza surveillance and situational awareness during different types of potentially health threatening events.ResultsWe found that syndromic surveillance systems might detect the onset or peak of seasonal influenza earlier if they analyse non-clinical data sources. Timely situational awareness during different types of events is supported by an automated syndromic surveillance system capable of analysing multiple syndromes. To our surprise, the analysis of multiple data sources was no key success factor for situational awareness.ConclusionsWe suggest to consider these key success factors when designing or further developing syndromic surveillance systems. Qualitative Comparative Analysis helped interpreting complex, mixed data on small-N cases and resulted in concrete and practically relevant findings.
| S-EPMC4868285 | biostudies-literature
Project description:Background: A rigorous approach is needed to inform rapid adaptation and optimisation of behavioral interventions in evolving public health contexts, such as the Covid-19 pandemic. This helps ensure that interventions are relevant, persuasive, and feasible while remaining evidence-based. This paper provides a set of iterative methods to rapidly adapt and optimize an intervention during implementation. These methods are demonstrated through the example of optimizing an effective online handwashing intervention called Germ Defense. Methods: Three revised versions of the intervention were rapidly optimized and launched within short timeframes of 1-2 months. Optimisations were informed by: regular stakeholder engagement; emerging scientific evidence, and changing government guidance; rapid qualitative research (telephone think-aloud interviews and open-text surveys), and analyses of usage data. All feedback was rapidly collated, using the Table of Changes method from the Person-Based Approach to prioritize potential optimisations in terms of their likely impact on behavior change. Written feedback from stakeholders on each new iteration of the intervention also informed specific optimisations of the content. Results: Working closely with clinical stakeholders ensured that the intervention was clinically accurate, for example, confirming that information about transmission and exposure was consistent with evidence. Patient and Public Involvement (PPI) contributors identified important clarifications to intervention content, such as whether Covid-19 can be transmitted via air as well as surfaces, and ensured that information about difficult behaviors (such as self-isolation) was supportive and feasible. Iterative updates were made in line with emerging evidence, including changes to the information about face-coverings and opening windows. Qualitative research provided insights into barriers to engaging with the intervention and target behaviors, with open-text surveys providing a useful supplement to detailed think-aloud interviews. Usage data helped identify common points of disengagement, which guided decisions about optimisations. The Table of Changes was modified to facilitate rapid collation and prioritization of multiple sources of feedback to inform optimisations. Engagement with PPI informed the optimisation process. Conclusions: Rapid optimisation methods of this kind may in future be used to help improve the speed and efficiency of adaptation, optimization, and implementation of interventions, in line with calls for more rapid, pragmatic health research methods.
| S-EPMC8109268 | biostudies-literature