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Students’ questions play an important role in meaningful learning and scientific inquiry. They are a potential resource for both teaching and learning science. Despite the capacity of students’ questions for enhancing learning, much of this potential still remains untapped. The purpose of this chapter, therefore, is to examine and review the existing research on students’ questions and to explore ways of advancing future work into this area. The chapter begins by highlighting the importance and role of students’ questions and the ways in which they have been categorized to argue that there are limitations to each of these. It then seeks to show, drawing on sets of classroom videos, that a schema based on the epistemic function of the question for constructing knowledge would suggest that there are really three categories of question – ontic questions, causal questions, and epistemic questions. The chapter then explores which programs of research offer promise for helping teachers to scaffold students at producing epistemic and better questions.
Prevention of Clostridioides difficile infection (CDI) is a national priority and may be facilitated by deployment of the Targeted Assessment for Prevention (TAP) Strategy, a quality improvement framework providing a focused approach to infection prevention. This article describes the process and outcomes of TAP Strategy implementation for CDI prevention in a healthcare system.
Hospital A was identified based on CDI surveillance data indicating an excess burden of infections above the national goal; hospitals B and C participated as part of systemwide deployment. TAP facility assessments were administered to staff to identify infection control gaps and inform CDI prevention interventions. Retrospective analysis was performed using negative-binomial, interrupted time series (ITS) regression to assess overall effect of targeted CDI prevention efforts. Analysis included hospital-onset, laboratory-identified C. difficile event data for 18 months before and after implementation of the TAP facility assessments.
The systemwide monthly CDI rate significantly decreased at the intervention (β2, −44%; P = .017), and the postintervention CDI rate trend showed a sustained decrease (β1 + β3; −12% per month; P = .008). At an individual hospital level, the CDI rate trend significantly decreased in the postintervention period at hospital A only (β1 + β3, −26% per month; P = .003).
This project demonstrates TAP Strategy implementation in a healthcare system, yielding significant decrease in the laboratory-identified C. difficile rate trend in the postintervention period at the system level and in hospital A. This project highlights the potential benefit of directing prevention efforts to facilities with the highest burden of excess infections to more efficiently reduce CDI rates.
We estimate Moody’s preference for accurate versus biased ratings using hand-collected data on the internal labor market outcomes of its analysts. We find that accurate analysts are more likely to be promoted and less likely to depart. The opposite is true for analysts who downgrade more frequently, who assign ratings below those predicted by a ratings model, and whose downgrades are associated with large negative market reactions. Downgraded firms are also more likely to be assigned a new analyst. These patterns are consistent with Moody’s balancing its desire for accuracy against its corporate clients’ desire for higher ratings.
Crisis resolution teams (CRTs) offer brief, intensive home treatment for people experiencing mental health crisis. CRT implementation is highly variable; positive trial outcomes have not been reproduced in scaled-up CRT care.
To evaluate a 1-year programme to improve CRTs’ model fidelity in a non-masked, cluster-randomised trial (part of the Crisis team Optimisation and RElapse prevention (CORE) research programme, trial registration number: ISRCTN47185233).
Fifteen CRTs in England received an intervention, informed by the US Implementing Evidence-Based Practice project, involving support from a CRT facilitator, online implementation resources and regular team fidelity reviews. Ten control CRTs received no additional support. The primary outcome was patient satisfaction, measured by the Client Satisfaction Questionnaire (CSQ-8), completed by 15 patients per team at CRT discharge (n = 375). Secondary outcomes: CRT model fidelity, continuity of care, staff well-being, in-patient admissions and bed use and CRT readmissions were also evaluated.
All CRTs were retained in the trial. Median follow-up CSQ-8 score was 28 in each group: the adjusted average in the intervention group was higher than in the control group by 0.97 (95% CI −1.02 to 2.97) but this was not significant (P = 0.34). There were fewer in-patient admissions, lower in-patient bed use and better staff psychological health in intervention teams. Model fidelity rose in most intervention teams and was significantly higher than in control teams at follow-up. There were no significant effects for other outcomes.
The CRT service improvement programme did not achieve its primary aim of improving patient satisfaction. It showed some promise in improving CRT model fidelity and reducing acute in-patient admissions.
Adequate fruit and vegetable intake is important in the prevention of chronic disease. Health literacy is associated with health outcomes but its role in dietary behaviour has received little attention. The present study investigated the association between a multidimensional measure of health literacy, sociodemographic characteristics, and fruit and vegetable intake in rural Australia.
A cross-sectional survey on intake of fruits and vegetables (servings/d), demographic characteristics and health literacy profile using a nine-scale Health Literacy Questionnaire (HLQ). Associations between health literacy and fruit and vegetable intake were assessed using logistic regression.
A large rural area of Victoria.
Adults residing in the Grampians region (n 1154; 61 % female, mean age 52 (sd 17) years).
The HLQ scale ‘Actively managing my health’ predicted (OR; 95 % CI) fruit (2·31; 1·87, 2·84) and vegetable (1·81; 1·45, 2·26) intake. The scales ‘Appraisal of health information’ (fruits: 1·73; 1·41, 2·13; vegetables: 1·49; 1·20, 1·86), ‘Social support for health’ (fruits: 1·31; 1·06, 1·63; vegetables: 1·40; 1·10, 1·76) and ‘Ability to find good health information’ (fruits: 1·25; 1·05, 1·48; vegetables: 1·36; CI 1·13, 1·63) also predicted fruit and vegetable intake. These associations remained significant after adjusting for age, gender, educational attainment and having private health insurance.
Health literacy, particularly being proactive, appraising information and having social support for health, is associated with greater fruit and vegetable intake. Future interventions should consider the health literacy needs of the community to improve fruit and vegetable intake.
Roll-call data have become a staple of contemporary scholarship on legislative behavior. Recent methodological innovations in the analysis of roll-call data have produced a number of important theoretical insights, such as understanding the structure of congressional decisionmaking and the role of parties and ideology in Congress. Many of the methodological innovations and theoretical questions sparked by congressional scholarship have been difficult to test at the state level because of the lack of comprehensive data on various forms of state legislative behavior, including roll-call voting. The Representation in America's Legislatures project rectifies that problem through collection of comprehensive state legislative roll-call votes across all 99 state legislative chambers for the 1999–2000 and 2003–04 legislative sessions. In this article, we describe the data available through this project as well as our data acquisition procedures, including Stata and Perl programming and OCR of paper documents, with suggestions about how to use these methods to collect a wide range of state-level data.
MATLAB is exceptionally strong in linear algebra, numerical methods, and graphical interpretation of data. It is easily programmed and relatively easy to learn to use. Hence, it has proven invaluable to engineers and scientists who rely on the scientific techniques and methods at which MATLAB excels. Very often the individuals and groups that so employ MATLAB are primarily interested in the numbers and graphs that emerge from MATLAB commands, processes and programs. Therefore, it is enough for them to work in a MATLAB Command Window, from which they can easily print or export their desired output.
However, other practitioners of mathematical software find themselves with two additional requirements. First, they need a mathematical software package embedded in an interactive environment, in which it is easy to make changes and regenerate results. Second, they need a higher-level presentation mode, which integrates computation and graphics with text, uses different formats for input and output, and communicates effortlessly with other software applications. These additional requirements can be accomplished using either cells and the publish command, or else the M-book interface, both of which were briefly described in Chapter 3. The present chapter goes into more detail and discusses some of the fine points of these methods.
Fine Points of Publishing
As we mentioned Chapter 3, the simplest way to produce a finished presentation with MATLAB is to prepare your work in a script M-file and then publish the result.
This is a short, focused introduction to MATLAB, a comprehensive software system for mathematical and technical computing. It contains concise explanations of essential MATLAB commands, as well as easily understood instructions for using MATLAB's programming features, graphical capabilities, simulation models, and rich desktop interface. Written for MATLAB 7, it can also be used with earlier (and later) versions of MATLAB. This book teaches how to graph functions, solve equations, manipulate images, and much more. It contains explicit instructions for using MATLAB's companion software, Simulink, which allows graphical models to be built for dynamical systems. MATLAB's new "publish" feature is discussed, which allows mathematical computations to be combined with text and graphics, to produce polished, integrated, interactive documents. For the beginner it explains everything needed to start using MATLAB, while experienced users making the switch to MATLAB 7 from an earlier version will also find much useful information here.
MATLAB is a high-level technical computing language and interactive environment for algorithm development, data visualization, data analysis, and numerical computation. Using MATLAB, you can solve technical computing problems faster than with traditional programming languages, such as C, C++, and Fortran. – The MathWorks, Inc.
That statement encapsulates the view of The MathWorks, Inc., the developer of MATLAB®. MATLAB 7 is an ambitious program. It contains hundreds of commands to do mathematics. You can use it to graph functions, solve equations, perform statistical tests, and much more. It is a high-level programming language that can communicate with its cousins, e.g., Fortran and C. You can produce sound and animate graphics. You can do simulations and modeling (especially if you have access not just to basic MATLAB but also to its accessory Simulink®). You can prepare materials for export to the World Wide Web. In addition, you can use MATLAB to combine mathematical computations with text and graphics in order to produce a polished, integrated, interactive document.
A program this sophisticated contains many features and options. There are literally hundreds of useful commands at your disposal. The MATLAB help documentation contains thousands of entries. The standard references, whether the MathWorks User's Guide for the product, or any of our competitors, contain a myriad of tables describing an endless stream of commands, options, and features that the user might be expected to learn or access.
In this chapter we describe an effective procedure for working with MATLAB, and for preparing and presenting the results of a MATLAB session. In particular we discuss some features of the MATLAB interface and the use of M-files. We introduce a new command in MATLAB 7, publish, which produces formatted output. We also give some simple hints for debugging your M-files.
The MATLAB Interface
Starting with version 6, MATLAB has an interface called the MATLAB Desktop. Embedded inside it is the Command Window that we described in Chapter 2.
By default, the MATLAB Desktop (Figure 1.1 in Chapter 1) contains four windows inside it, the Command Window on the right, the Current Directory Browser and the Workspace Browser in the upper left, and the Command History Window in the lower left. Notice that there are tabs for alternating between the Current Directory and Workspace Browsers. You can change which windows are currently visible with the Desktop menu (in MATLAB 6, the View menu) at the top of the Desktop, and you can adjust the sizes of the windows by dragging their edges with the mouse. The Command Window is where you type the commands and instructions that cause MATLAB to evaluate, compute, draw, and perform all the other wonderful magic that we describe in this book. We will discuss the other windows in separate sections below.
With MATLAB you can create your own Graphical User Interface, or GUI, which consists of a Figure window containing menus, buttons, text, graphics, etc., that a user can manipulate interactively with the mouse and keyboard. There are two main steps in creating a GUI: one is designing its layout, and the other is writing callback functions that perform the desired operations when the user selects different features.
GUI Layout and GUIDE
Specifying the location and properties of various objects in a GUI can be done with commands such as uicontrol, uimenu, and uicontextmenu in an M-file. MATLAB also provides an interactive tool (a GUI itself!) called GUIDE (this stands for Graphical User Interface Development Environment) that greatly simplifies the task of building a GUI. We will describe here how to get started writing GUIs with the MATLAB 7 version of GUIDE, which has some significant enhancements over earlier versions. The version of GUIDE in MATLAB 6 is roughly similar, but some of the menu items and options are different or missing.
✓ One possible drawback of GUIDE is that it equips your GUI with commands that are new in MATLAB 7 and it saves the layout of the GUI in a binary.fig file. If your goal is to create a robust GUI that many different users can use with different versions of MATLAB, you may be better off writing the GUI from scratch as an M-file.
Every time you create an M-file, you are writing a computer program using the MATLAB programming language. You can do quite a lot in MATLAB using no more than the most basic programming techniques that we have already introduced. In particular, we discussed simple loops (using for) and a rudimentary approach to debugging in Chapter 3. In this chapter, we will cover some further programming commands and techniques that are useful for attacking more complicated problems with MATLAB. If you are already familiar with another programming language, much of this material will be quite easy for you to pick up!
✓ Many MATLAB commands are themselves M-files, which you can examine using type or edit, e.g., enter type isprime to see the M-file for the command isprime. You can learn a lot about MATLAB programming techniques by inspecting the built-in M-files.
For many user-defined functions, you can use a function M-file that executes the same sequence of commands for each input. However, one often wants a function to perform a different sequence of commands in different cases, depending on the input. You can accomplish this with a branching command, and, as in many other programming languages, branching in MATLAB is usually done with the command if, which we will discuss now. Later we will describe the other main branching command, switch.
Branching with if
For a simple illustration of branching with if, consider the following function M-file absval.m, which computes the absolute value of a real number.