Short-term health and social care benefits of the Family Nurse Partnership lack evidence in the UK context but there is promise for child developmental outcomes

Nurse Family Partnership (NFP) home visiting from pregnancy to 24 months post partum, guided by a manualised curriculum, has been shown in three randomised controlled trials (RCTs) to have multiple beneficial outcomes and to be a cost-effective way to decrease the risk of child abuse for children of young, psychologically vulnerable first-time mothers.1 NFP has also been shown to strengthen families through increased maternal employment and wider spacing of pregnancies, and has demonstrated a range of benefits for children through adolescence.2 The US-developed programme was introduced into England in 2007 (renamed Family Nurse Partnership, FNP) and a pragmatic, non-blinded RCT was launched in 2009

Research utilisation and knowledge mobilisation in the commissioning and joint planning of public health interventions to reduce alcohol-related harms: a qualitative case design using a cocreation approach

Background: Considerable resources are spent on research to establish what works to improve the nation’s health. If the findings from this research are used, better health outcomes can follow, but we know that these findings are not always used. In public health, evidence of what works may not ‘fit’ everywhere, making it difficult to know what to do locally. Research suggests that evidence use is a social and dynamic process, not a simple application of research findings. It is unclear whether it is easier to get evidence used via a legal contracting process or within unified organisational arrangements with shared responsibilities. Objective: To work in cocreation with research participants to investigate how research is utilised and knowledge mobilised in the commissioning and planning of public health services to reduce alcohol-related harms. Design, setting and participants: Two in-depth, largely qualitative, cross-comparison case studies were undertaken to compare real-time research utilisation in commissioning across a purchaser–provider split (England) and in joint planning under unified organisational arrangements (Scotland) to reduce alcohol-related harms. Using an overarching realist approach and working in cocreation, case study partners (stakeholders in the process) picked the topic and helped to interpret the findings. In Scotland, the topic picked was licensing; in England, it was reducing maternal alcohol consumption. Methods: Sixty-nine interviews, two focus groups, 14 observations of decision-making meetings, two local feedback workshops (n = 23 and n = 15) and one national workshop (n = 10) were undertaken. A questionnaire (n = 73) using a Behaviourally Anchored Rating Scale was issued to test the transferability of the 10 main findings. Given the small numbers, care must be taken in interpreting the findings. Findings: Not all practitioners have the time, skills or interest to work in cocreation, but when there was collaboration, much was learned. Evidence included professional and tacit knowledge, and anecdotes, as well as findings from rigorous research designs. It was difficult to identify evidence in use and decisions were sometimes progressed in informal ways and in places we did not get to see. There are few formal evidence entry points. Evidence (prevalence and trends in public health issues) enters the process and is embedded in strategic documents to set priorities, but local data were collected in both sites to provide actionable messages (sometimes replicating the evidence base). Conclusions: Two mid-range theories explain the findings. If evidence has saliency (relates to ‘here and now’ as opposed to ‘there and then’) and immediacy (short, presented verbally or visually and with emotional appeal) it is more likely to be used in both settings. A second mid-range theory explains how differing tensions pull and compete as feasible and acceptable local solutions are pursued across stakeholders. Answering what works depends on answering for whom and where simultaneously to find workable (if temporary) ‘blends’. Gaining this agreement across stakeholders appeared more difficult across the purchaser–provider split, because opportunities to interact were curtailed; however, more research is needed. Funding: This study was funded by the Health Services and Delivery Research programme of the National Institute for Health Research

Designing educational technologies in the age of AI: A learning sciences‐driven approach

Interdisciplinary research from the learning sciences has helped us understand a great deal about the way that humans learn, and as a result we now have an improved understanding about how best to teach and train people. This same body of research must now be used to better inform the development of Artificial Intelligence (AI) technologies for use in education and training. In this paper, we use three case studies to illustrate how learning sciences research can inform the judicious analysis, of rich, varied and multimodal data, so that it can be used to help us scaffold students and support teachers. Based on this increased understanding of how best to inform the analysis of data through the application of learning sciences research, we are better placed to design AI algorithms that can analyse rich educational data at speed. Such AI algorithms and technology can then help us to leverage faster, more nuanced and individualised scaffolding for learners. However, most commercial AI developers know little about learning sciences research, indeed they often know little about learning or teaching. We therefore argue that in order to ensure that AI technologies for use in education and training embody such judicious analysis and learn in a learning sciences informed manner, we must develop inter-stakeholder partnerships between AI developers, educators and researchers. Here, we exemplify our approach to such partnerships through the EDUCATE Educational Technology (EdTech) programme. Practitioner Notes What is already known about this topic? The progress of AI Technology and learning analytics lags behind the adoption of these approaches and technologies in other fields such as medicine or finance. Data are central to the empirical work conducted in the learning sciences and to the development of machine learning Artificial Intelligence (AI). Education is full of doubts about the value that any technology can bring to the teaching and learning process. What this paper adds? We argue that the learning sciences have an important role to play in the design of educational AI, through their provision of theories that can be operationalised and advanced. Through case studies, we illustrate that the analysis of data appropriately informed by interdisciplinary learning sciences research can be used to power AI educational technology. We provide a framework for inter-stakeholder, interdisciplinary partnerships that can help educators better understand AI, and AI developers better understand education. Implications for practice and/or policy? AI is here to stay and that it will have an increasing impact on the design of technology for use in education and training. Data, which is the power behind machine learning AI, can enable analysis that can vastly increase our understanding of when and how the teaching and learning process is progressing positively. Inter-stakeholder, interdisciplinary partnerships must be used to make sure that AI provides some of the educational benefits its application in other areas promise us