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Few interventions that succeed in improving healthcare locally end up becoming spread and sustained more widely. This indicates that we need to think differently about spreading improvements in practice. Drawing on a focused review of academic and grey literature, the authors outline how spread, scale-up, and sustainability have been defined and operationalised, highlighting areas of ambiguity and contention. Following an overview of relevant frameworks and models, they focus on three specific approaches and unpack their theoretical assumptions and practical implications: the Dynamic Sustainability Framework, the 3S (structure, strategy, supports) infrastructure approach for scale-up, and the NASSS (non-adoption, abandonment, and challenges to scale-up, spread, and sustainability) framework. Key points are illustrated through empirical case narratives and the Element concludes with actionable learning for those engaged in improvement activities and for researchers. This title is also available as Open Access on Cambridge Core.
This chapter introduces what we mean by big data, its importance, and the key principles for handling it efficiently. The term “big data” is frequently used to refer to the idea of exploiting lots of data to obtain some benefit, but there is no standard definition. It is also commonly known as large-scale data processing or data-intensive applications. We discuss the key components of big data, and how it is not all about volume, but also other aspects such as velocity and variety need to be considered. The world of big data has multiple faces such as databases, infrastructure, and security, but we focus on data analytics. Then, we cover how to deal with big data, explaining why we cannot scale up using a single computer, but we must scale out and use multiple machines to process the data. We suggest why traditional high-performance computing clusters are not appropriate for data-intensive applications and how they would collapse the network. Finally, we introduce key features of a big data cluster such as not being focused on pure computation, no need for high-end computers, the need for fault tolerance mechanisms, and respecting the principle of data locality.
This chapter puts forward new guidelines for designing and implementing distributed machine learning algorithms for big data. First, we present two different alternatives, which we call local and global approaches. To show how these two strategies work, we focus on the classical decision tree algorithm, revising its functioning and some details that need modification to deal with large datasets. We implement a local-based solution for decision trees, comparing its behavior and efficiency against a sequential model and the MLlib version. We also discuss the nitty-gritty of the implementation of decision trees in MLlib as a great example of a global solution. That allows us to formally define these two concepts, discussing the key (expected) advantages and disadvantages. The second part is all about measuring the scalability of a big data solution. We talk about three classical metrics, speed-up, size-up, and scale-up, to help understand if a distributed solution is scalable. Using these, we test our local-based approach and compare it against its global counterpart. This experiment allows us to give some tips for calculating these metrics correctly using a Spark cluster.
Early childhood education and care (ECEC) is a recommended setting for the delivery of health eating interventions ‘at scale’ (i.e. to large numbers of childcare services) to improve child public health nutrition. Appraisal of the ‘scalability’ (suitability for delivery at scale) of interventions is recommended to guide public health decision-making. This study describes the extent to which factors required to assess scalability are reported among ECEC-based healthy eating interventions.
Design:
Studies from a recent Cochrane systematic review assessing the effectiveness of healthy eating interventions delivered in ECEC for improving child dietary intake were included. The reporting of factors of scalability was assessed against domains outlined within the Intervention Scalability Assessment Tool (ISAT). The tool recommends decision makers consider the problem, the intervention, strategic and political context, effectiveness, costs, fidelity and adaptation, reach and acceptability, delivery setting and workforce, implementation infrastructure and sustainability. Data were extracted by one reviewer and checked by a second reviewer.
Setting:
ECEC.
Participants:
Children 6 months to 6 years.
Results:
Of thirty-eight included studies, none reported all factors within the ISAT. All studies reported the problem, the intervention, effectiveness and the delivery workforce and setting. The lowest reported domains were intervention costs (13 % of studies) and sustainability (16 % of studies).
Conclusions:
Findings indicate there is a lack of reporting of some key factors of scalability for ECEC-based healthy eating interventions. Future studies should measure and report such factors to support policy and practice decision makers when selecting interventions to be scaled-up.
Chapter 14 describes the biotechnological applications of recombinant DNA technology. The range of disciplines that contribute to biotechnology is outlined to illustrate the scale and scope of the sector. Production of proteins is one key area where cloned genes can be expressed to produce high-value products for use in a variety of applications, and the types of systems used for protein production are discussed. Protein engineering by methods such as rational design and directed evolution has enabled customised proteins to be developed for specific applications. The requirements for transition from laboratory-scale research and development to industrial production at a commercially viable level are outlined, and the contribution of the biotechnology sector in managing the COVID-19 pandemic is discussed.
Transform your research into commercial biomedical products with this revised and updated second edition. Covering drugs, devices and diagnostics, this book provides a step-by-step introduction to the process of commercialization, and will allow you to create a realistic business plan to develop your ideas into approved biomedical technologies. This new edition includes: Over 25% new material, including practical tips on startup creation from experienced entrepreneurs. Tools for starting, growing and managing a new venture, including business planning and commercial strategy, pitching investors, and managing operations.Global real-world case studies, including emerging technologies such as regulated medical software and Artificial Intelligence (AI), offer insights into key challenges and help illustrate complex points. Tips and operational tools from established industry insiders, suitable for graduate students and new biomedical entrepreneurs.
The Clinical and Translational Science Award (CTSA) Program is a Consortium of nearly 60 academic medical research centers across the USA and a natural network for evaluating the spread and uptake of translational research innovation across the Consortium.
Methods:
Dissemination of the Accrual to Clinical Trials (ACT) Network, a federated clinical informatics data network for population-based cohort discovery, began January 2018 across the Consortium. Diffusion of innovation theory guided dissemination design and evaluation. Mixed-methods assessed the spread and uptake across the Consortium through July 1, 2019 (n = 48 CTSAs). Methods included prospective time activity tracking (Kaplan–Meier curves), and survey and qualitative interviews.
Results:
Within 18 months, nearly 80% of CTSAs had joined the data network and two-thirds of CTSAs achieving technical readiness had initiated launch to local clinical investigators. Over 10,000 ACT Network queries are projected for 2019; and by 2020, nearly all CTSAs will have joined the network. Median time-from-technical-readiness-to-local-launch was 154 days (interquartile range: 87–225 days]. Quality improvement processes reduced time-to-launch by 35.2% (64 days, p = 0.0036). Lessons learned include: (1) conceptualize dissemination as two-stage adoption demonstrating value for both CTSA hub service providers and clinical investigators; (2) include institutional trial into dissemination strategies so CTSA hubs can refine internal workflows and gather local user feedback endorsement; (3) embrace designing-for-dissemination during technology development; and (4) sustain adaptive dissemination and customer relationship management to keep CTSA hubs and users engaged.
Conclusions:
Scale-up and spread of the ACT Network provides lessons learned for others disseminating innovation across the CTSA Consortium. The Network is primed for embedded implementation research.
This chapter interrogates assumptions behind the models used both in cost-effectiveness analysis, and to set global targets. The models neglected to address how human rights realities, such as health sector discrimination and legal barriers, might undermine the optimistic scenarios the models predicted would result from scale-up of testing and treatment. The lack of quantitative research showing that addressing human rights would have a measurable impact on health, and that such work was cost-effective, meant that it was easy to exclude these and similarly unquantified considerations from biomedical scael-up. Thus in many countries, the work of addressing stigma, discrimination, criminalization, and gender inequality, while frequently cited as rhetorically important, is in practice an afterthought in planning, financing and implementing the HIV response. The second part of the chapter returns to Grenada to observe community activists and health officials wrestling with the challenges of quantification, as they debate which questions to ask in the study. While the global mathematical models aimed at simplicity in order to drive decision-makers to prioritize funding HIV programs, the CVC study wrestled with the problem of how best to capture local complexities and protect the fragile thread of trust they were beginning to establish with hidden communities.
There is a dearth of information on how to scale-up evidence-based psychological interventions, particularly within the context of existing HIV programs. This paper describes a strategy for the scale-up of an intervention delivered by lay health workers (LHWs) to 60 primary health care facilities in Zimbabwe.
Methods
A mixed methods approach was utilized as follows: (1) needs assessment using a semi-structured questionnaire to obtain information from nurses (n = 48) and focus group discussions with District Health Promoters (n = 12) to identify key priority areas; (2) skills assessment to identify core competencies and current gaps of LHWs (n = 300) employed in the 60 clinics; (3) consultation workshops (n = 2) with key stakeholders to determine referral pathways; and (4) in-depth interviews and consultations to determine funding mechanisms for the scale-up.
Results
Five cross-cutting issues were identified as critical and needing to be addressed for a successful scale-up. These included: the lack of training in mental health, unavailability of psychiatric drugs, depleted clinical staff levels, unavailability of time for counseling, and poor and unreliable referral systems for people suffering with depression. Consensus was reached by stakeholders on supervision and support structure to address the cross-cutting issues described above and funding was successfully secured for the scale-up.
Conclusion
Key requirements for success included early buy-in from key stakeholders, extensive consultation at each point of the scale-up journey, financial support both locally and externally, and a coherent sustainability plan endorsed by both government and private sectors.
To study the implementation of a school-based healthy eating (HE) model one year after scale-up in British Columbia (BC). Specifically, to examine implementation of Action Schools! BC (AS! BC) and its influence on implementation of classroom HE activities, and to explore factors associated with implementation.
Design
Diffusion of Innovations, Social Cognitive and Organizational Change theories guided our approach. We used a mixed-methods research design including focus group interviews (seven schools, sixty-two implementers) and a cross-sectional multistage survey to principals (n 36, 92 % response rate) and teachers of grades 4 to 7 (n 168, 70 % response rate). Self-reported implementation of classroom HE activities and reported use of specific AS! BC HE activities were primary implementation measures. Thematic analysis of focus group data and multilevel mixed-effect logistic regression analyses of survey data were conducted.
Setting
Elementary schools across BC, Canada.
Subjects
Thirty-nine school districts, thirty-six principals, 168 grade 4 to 7 teachers.
Results
Forty-two per cent of teachers in registered schools were implementing AS! BC HE in their classrooms. Users were 6·25 times more likely to have delivered a HE lesson in the past week. Implementation facilitators were school champions, technical support and access to resources; barriers were lack of time, loss of leadership or momentum. Implementation predictors were teacher training, self-efficacy, experience with the physical activity component of AS! BC, supportive school climate and parental post-secondary education.
Conclusions
Our findings reinforce that continued teacher training and support are important public health investments that contribute to successful implementation of school-based HE models after scale-up.
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