Strategies for teaching evidence-based practice to undergraduate health students: A systematic review protocol

Review question/objective The objective of this review is to find, critically appraise and synthesize the available quantitative evidence on the effectiveness of interventions that promote successful teaching of the evidence-based practice process in undergraduate health students, in preparation for them to become professional evidence-based practitioners. More specifically, the question that this review seeks to answer is: What is the effectiveness of teaching strategies for evidence-based practice for undergraduate health students? Inclusion criteria Types of participants This review will consider studies that include undergraduate health students from any undergraduate health discipline, including but not limited to medicine, nursing and allied health. Post graduate and post-registration students will not be included. Types of interventions This review will consider studies that evaluate strategies or interventions aimed at teaching any or all of the five steps of evidence-based practice, namely asking a structured clinical question; collecting the best evidence available; critically appraising the evidence to ensure validity, relevance and applicability; applying or integrating the results into clinical practice, and evaluating outcomes. The strategy may take place solely within a tertiary education environment or may be combined with a clinical setting. Types of outcomes This review will consider studies that include the following outcome measures: evidence-based practice behavior, knowledge, skills, attitudes, self-efficacy (or self-confidence), beliefs, values, intention to use evidence-based practice (future use) and confidence levels. Tools used to measure these outcomes will be assessed for reported validity, reliability and generalizability. Outcomes will be measured during the student’s education period up to graduation. If studies are conducted across different year levels this will be taken into account during analysis and reported accordingly.

Using DNA as a drug - bioprocessing and delivery strategies

DNA may take a leading role in a future generation of blockbuster therapeutics. DNA has inherent advantages over other biomolecules such as protein, RNA and virus-like particles including safety, production simplicity and higher stability at ambient temperatures. Vaccination is the principal measure for preventing influenza and reducing the impact of pandemics; however, vaccines take up to 8-9 months to produce, and the global production capacity is woefully low. With production times as short as 2 weeks, improved safety and stability, bioprocess engineering developments, and the ability to perform numerous therapeutic roles, DNA has the potential to meet the demands of emerging and existing diseases. DNA is experiencing sharp growths in demand as indicated by its use in gene therapy trials and DNA vaccine related patents. Of particular interest for therapeutic use is plasmid DNA (pDNA), a form of non-genomic DNA that makes use of cellular machinery to express proteins or antigens. The production stages of fermentation and downstream purification are considered in this article. Forward looking approaches to purifying and delivering DNA are reported, including affinity chromatography and nasal inhalation. The place that pDNA may take in the preparation for and protection against pandemics is considered. If DNA therapeutics and vaccines prove to be effective, the ultimate scale of production will be huge which shall require associated bioprocess engineering research and development for purification of this large, unique biomolecule.

Implementing the self-management threshold learning outcome for law : some intentional design strategies from the current curriculum toolbox

There is a growing awareness of the high levels of psychological distress being experienced by law students and the practising profession in Australia. In this context, a Threshold Learning Outcome (TLO) on self-management has been included in the six TLOs recently articulated as minimum learning outcomes for all Australian graduates of the Bachelor of Laws degree (LLB). The TLOs were developed during 2010 as part of the Australian Learning and Teaching Council’s (ALTC’s) project funded by the Australian Government to articulate ‘Learning and Teaching Academic Standards’. The TLOs are the result of a comprehensive national consultation process led by the ALTC’s Discipline Scholars: Law, Professors Sally Kift and Mark Israel.1 The TLOs have been endorsed by the Council of Australian Law Deans (CALD) and have received broad support from members of the judiciary and practising profession, representative bodies of the legal profession, law students and recent graduates, Legal Services Commissioners and the Law Admissions Consultative Committee. At the time of writing, TLOs for the Juris Doctor (JD) are also being developed, utilising the TLOs articulated for the LLB as their starting point but restating the JD requirements as the higher order outcomes expected of graduates of a ‘Masters Degree (Extended)’, this being the award level designation for the JD now set out in the new Australian Qualifications Framework.2 As Australian law schools begin embedding the learning, teaching and assessment of the TLOs in their curricula, and seek to assure graduates’ achievement of them, guidance on the implementation of the self-management TLO is salient and timely.

Scale adaptive tracking using mean shift and efficient feature matching

The mean shift tracker has achieved great success in visual object tracking due to its efficiency being nonparametric. However, it is still difficult for the tracker to handle scale changes of the object. In this paper, we associate a scale adaptive approach with the mean shift tracker. Firstly, the target in the current frame is located by the mean shift tracker. Then, a feature point matching procedure is employed to get the matched pairs of the feature point between target regions in the current frame and the previous frame. We employ FAST-9 corner detector and HOG descriptor for the feature matching. Finally, with the acquired matched pairs of the feature point, the affine transformation between target regions in the two frames is solved to obtain the current scale of the target. Experimental results show that the proposed tracker gives satisfying results when the scale of the target changes, with a good performance of efficiency.

Doubly Fed Induction Generators : Overview and Intelligent Control Strategies for Wind Energy Conversion Systems

Adjustable speed induction generators, especially the Doubly-Fed Induction Generators (DFIG) are becoming increasingly popular due to its various advantages over fixed speed generator systems. A DFIG in a wind turbine has ability to generate maximum power with varying rotational speed, ability to control active and reactive by integration of electronic power converters such as the back-to-back converter, low rotor power rating resulting in low cost converter components, etc, DFIG have become very popular in large wind power conversion systems. This chapter presents an extensive literature survey over the past 25 years on the different aspects of DFIG. Application of H8 Controller for enhanced DFIG-WT performance in terms of robust stability and reference tracking to reduce mechanical stress and vibrations is also demonstrated in the chapter.

Adaptive online gradient descent

We study the rates of growth of the regret in online convex optimization. First, we show that a simple extension of the algorithm of Hazan et al eliminates the need for a priori knowledge of the lower bound on the second derivatives of the observed functions. We then provide an algorithm, Adaptive Online Gradient Descent, which interpolates between the results of Zinkevich for linear functions and of Hazan et al for strongly convex functions, achieving intermediate rates between [square root T] and [log T]. Furthermore, we show strong optimality of the algorithm. Finally, we provide an extension of our results to general norms.

She’s done alright for a girl : strategies for teaching women artists

While evidence suggests that up to 65% of visual arts graduates in Australia are women, women artists are still dramatically under-represented in most sectors of the industry, from institutional exhibitions through to commercial gallery representation. Gender awareness in art school education was a prominent aspect of second wave feminist activism in this country, however the outcomes for women artists, particularly as their careers proceed, often remain discouraging. Over approximately the past ten years, the Visual Arts discipline at Queensland University of Technology has integrated a range of gender awareness strategies into its teaching program across both studio practice and history/theory areas, with relatively strong outcomes amongst female graduates, both as artists and arts workers. Employing practitioner reflection and praxis-based research, this paper takes stock of the approaches that have been trialled over this period and reflects on the combination of both explicit and implicit strategies employed, as well as student responses to them.

The Threshold Learning Outcome on self-management for the Bachelor of Laws degree : a proposed focus for teaching strategies in the first year law curriculum

The Australian Learning and Teaching Council (ALTC) Discipline Scholars for Law, Professors Sally Kift and Mark Israel, articulated six Threshold Learning Outcomes (TLOs) for the Bachelor of Laws degree as part of the ALTC’s 2010 project on Learning and Teaching Academic Standards. One of these TLOs promotes the learning, teaching and assessment of self-management skills in Australian law schools. This paper explores the concept of self-management and how it can be relevantly applied in the first year of legal education. Recent literature from the United States (US) and Australia provides insights into the types of issues facing law students, as well as potential antidotes to these problems. Based on these findings, I argue that designing a pedagogical framework for the first year law curriculum that promotes students’ connection with their intrinsic interests, values, motivations and purposes will facilitate student success in terms of their personal well-being, ethical dispositions and academic engagement.

Toward the adaptive generation of bespoke game content

In this chapter, we explore methods for automatically generating game content—and games themselves—adapted to individual players in order to improve their playing experience or achieve a desired effect. This goes beyond notions of mere replayability and involves modeling player needs to maximize their enjoyment, involvement, and interest in the game being played. We identify three main aspects of this process: generation of new content and rule sets, measurement of this content and the player, and adaptation of the game to change player experience. This process forms a feedback loop of constant refinement, as games are continually improved while being played. Framed within this methodology, we present an overview of our recent and ongoing research in this area. This is illustrated by a number of case studies that demonstrate these ideas in action over a variety of game types, including 3D action games, arcade games, platformers, board games, puzzles, and open-world games. We draw together some of the lessons learned from these projects to comment on the difficulties, the benefits, and the potential for personalized gaming via adaptive game design.

Managing moose harvests by the seat of your pants

Moose populations are managed for sustainable yield balanced against costs caused by damage to forestry or agriculture and collisions with vehicles. Optimal harvests can be calculated based on a structured population model driven by data on abundance and the composition of bulls, cows, and calves obtained by aerial-survey monitoring during winter. Quotas are established by the respective government agency and licenses are issued to hunters to harvest an animal of specified age or sex during the following autumn. Because the cost of aerial monitoring is high, we use a Management Strategy Evaluation to evaluate the costs and benefits of periodic aerial surveys in the context of moose management. Our on-the-fly "seat of your pants" alternative to independent monitoring is management based solely on the kill of moose by hunters, which is usually sufficient to alert the manager to declines in moose abundance that warrant adjustments to harvest strategies. Harvests are relatively cheap to monitor; therefore, data can be obtained each year facilitating annual adjustments to quotas. Other sources of "cheap" monitoring data such as records of the number of moose seen by hunters while hunting also might be obtained, and may provide further useful insight into population abundance, structure and health. Because conservation dollars are usually limited, the high cost of aerial surveys is difficult to justify when alternative methods exist. © 2012 Elsevier Inc.

Ecosystem management along ephemeral rivers: Trading off socio-economic water supply and vegetation conservation under flood regime uncertainty

In ecosystems driven by water availability, plant community dynamics depend on complex interactions between vegetation, hydrology, and human water resources use. Along ephemeral rivers—where water availability is erratic—vegetation and people are particularly vulnerable to changes in each other's water use. Sensible management requires that water supply be maintained for people, while preserving ecosystem health. Meeting such requirements is challenging because of the unpredictable water availability. We applied information gap decision theory to an ecohydrological system model of the Kuiseb River environment in Namibia. Our aim was to identify the robustness of ecosystem and water management strategies to uncertainties in future flood regimes along ephemeral rivers. We evaluated the trade-offs between alternative performance criteria and their robustness to uncertainty to account for both (i) human demands for water supply and (ii) reducing the risk of species extinction caused by water mining. Increasing uncertainty of flood regime parameters reduced the performance under both objectives. Remarkably, the ecological objective (species coexistence) was more sensitive to uncertainty than the water supply objective. However, within each objective, the relative performance of different management strategies was insensitive to uncertainty. The ‘best’ management strategy was one that is tuned to the competitive species interactions in the Kuiseb environment. It regulates the biomass of the strongest competitor and, thus, at the same time decreases transpiration, thereby increasing groundwater storage and reducing pressure on less dominant species. This robust mutually acceptable strategy enables species persistence without markedly reducing the water supply for humans. This study emphasises the utility of ecohydrological models for resource management of water-controlled ecosystems. Although trade-offs were identified between alternative performance criteria and their robustness to uncertain future flood regimes, management strategies were identified that help to secure an ecologically sustainable water supply.

Efficient minimax strategies for square loss games

We consider online prediction problems where the loss between the prediction and the outcome is measured by the squared Euclidean distance and its generalization, the squared Mahalanobis distance. We derive the minimax solutions for the case where the prediction and action spaces are the simplex (this setup is sometimes called the Brier game) and the \ell_2 ball (this setup is related to Gaussian density estimation). We show that in both cases the value of each sub-game is a quadratic function of a simple statistic of the state, with coefficients that can be efficiently computed using an explicit recurrence relation. The resulting deterministic minimax strategy and randomized maximin strategy are linear functions of the statistic.

Second-order quantile methods for experts and combinatorial games

We aim to design strategies for sequential decision making that adjust to the difficulty of the learning problem. We study this question both in the setting of prediction with expert advice, and for more general combinatorial decision tasks. We are not satisfied with just guaranteeing minimax regret rates, but we want our algorithms to perform significantly better on easy data. Two popular ways to formalize such adaptivity are second-order regret bounds and quantile bounds. The underlying notions of 'easy data', which may be paraphrased as "the learning problem has small variance" and "multiple decisions are useful", are synergetic. But even though there are sophisticated algorithms that exploit one of the two, no existing algorithm is able to adapt to both. In this paper we outline a new method for obtaining such adaptive algorithms, based on a potential function that aggregates a range of learning rates (which are essential tuning parameters). By choosing the right prior we construct efficient algorithms and show that they reap both benefits by proving the first bounds that are both second-order and incorporate quantiles.

Bicycle commuting and exposure to air pollution: A questionnaire-based investigation of perceptions, symptoms and risk management strategies

Background An increase in bicycle commuting participation may improve public health and traffic congestion in cities. Information on air pollution exposure (such as perception, symptoms and risk management) contributes to the responsible promotion of bicycle commuting participation. Methods To determine perceptions, symptoms and willingness for specific exposure risk management strategies of exposure to air pollution, a questionnaire-based cross-sectional investigation was conducted with adult bicycle commuters (n = 153; age = 41 ± 11 yr; 28% female). Results Frequency of acute respiratory signs and symptoms are positively-associated with in- and post-commute compared to pre-commute time periods (p < 0.05); greater positive-association is with respiratory disorder compared to healthy, and female compared to male, participants. The perception (although not signs or symptoms) of in-commute exposure to air pollution is positive-associated with the estimated level of in-commute proximity to motorised traffic. The majority of participants indicated a willingness (which varied with health status and gender) to adopt risk management strategies (with certain practical features) if shown to be appropriate and effective. Conclusions While acute signs and symptoms of air pollution exposure are indicated with bicycle commuting, and more so in susceptible individuals, there is willingness to manage exposure risk by adopting effective strategies with desirable features.

Optimizing preventative maintenance strategies for linear assets

Linear assets are engineering infrastructure, such as pipelines, railway lines, and electricity cables, which span long distances and can be divided into different segments. Optimal management of such assets is critical for asset owners as they normally involve significant capital investment. Currently, Time Based Preventive Maintenance (TBPM) strategies are commonly used in industry to improve the reliability of such assets, as they are easy to implement compared with reliability or risk-based preventive maintenance strategies. Linear assets are normally of large scale and thus their preventive maintenance is costly. Their owners and maintainers are always seeking to optimize their TBPM outcomes in terms of minimizing total expected costs over a long term involving multiple maintenance cycles. These costs include repair costs, preventive maintenance costs, and production losses. A TBPM strategy defines when Preventive Maintenance (PM) starts, how frequently the PM is conducted and which segments of a linear asset are operated on in each PM action. A number of factors such as required minimal mission time, customer satisfaction, human resources, and acceptable risk levels need to be considered when planning such a strategy. However, in current practice, TBPM decisions are often made based on decision makers’ expertise or industrial historical practice, and lack a systematic analysis of the effects of these factors. To address this issue, here we investigate the characteristics of TBPM of linear assets, and develop an effective multiple criteria decision making approach for determining an optimal TBPM strategy. We develop a recursive optimization equation which makes it possible to evaluate the effect of different maintenance options for linear assets, such as the best partitioning of the asset into segments and the maintenance cost per segment.