Exact Bayesian curve fitting and signal segmentation.

We consider regression models where the underlying functional relationship between the response and the explanatory variable is modeled as independent linear regressions on disjoint segments. We present an algorithm for perfect simulation from the posterior distribution of such a model, even allowing for an unknown number of segments and an unknown model order for the linear regressions within each segment. The algorithm is simple, can scale well to large data sets, and avoids the problem of diagnosing convergence that is present with Monte Carlo Markov Chain (MCMC) approaches to this problem. We demonstrate our algorithm on standard denoising problems, on a piecewise constant AR model, and on a speech segmentation problem

Creating an Instrument to Measure Student Response to Instructional Practices

BackgroundCalls for the reform of education in science, technology, engineering, and mathematics (STEM) have inspired many instructional innovations, some research based. Yet adoption of such instruction has been slow. Research has suggested that students’ response may significantly affect an instructor’s willingness to adopt different types of instruction.PurposeWe created the Student Response to Instructional Practices (StRIP) instrument to measure the effects of several variables on student response to instructional practices. We discuss the step‐by‐step process for creating this instrument.Design/MethodThe development process had six steps: item generation and construct development, validity testing, implementation, exploratory factor analysis, confirmatory factor analysis, and instrument modification and replication. We discuss pilot testing of the initial instrument, construct development, and validation using exploratory and confirmatory factor analyses.ResultsThis process produced 47 items measuring three parts of our framework. Types of instruction separated into four factors (interactive, constructive, active, and passive); strategies for using in‐class activities into two factors (explanation and facilitation); and student responses to instruction into five factors (value, positivity, participation, distraction, and evaluation).ConclusionsWe describe the design process and final results for our instrument, a useful tool for understanding the relationship between type of instruction and students’ response.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/136692/1/jee20162_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/136692/2/jee20162.pd

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