Using lecture recordings to support curriculum changes in the European convergence process

The aim of this paper is to present the experience of using lecture recordings to support curriculum changes within the framework of the European convergence process, mainly courses that need to be promoted or discontinued. We will explain an integrated solution for recording lectures consisting of a web portal, a videoconferencing tool and an economical and easily transportable kit. The validation process was performed recording three different courses at the Universidad Politécnica of Madrid (UPM) and using different diffusion channels, such as Moodle, an open source web portal called GlobalPlaza that supports streaming and recordings and the YouTube UPM channel. To assess the efficiency of our solution, a formal evaluation was conducted and will be also presented in this paper. The results show that lecture recordings allow teachers to support discontinued and new courses and enable students from remote areas to participate in international educational programmes, also the resulting recordings will be used as learning objects for future virtual courses.

Validation of assembled nucleic acid-based tests in diagnostic microbiology laboratories

Medical microbiology and virology laboratories use nucleic acid tests (NAT) to detect genomic material of infectious organisms in clinical samples. Laboratories choose to perform assembled (or in-house) NAT if commercial assays are not available or if assembled NAT are more economical or accurate. One reason commercial assays are more expensive is because extensive validation is necessary before the kit is marketed, as manufacturers must accept liability for the performance of their assays, assuming their instructions are followed. On the other hand, it is a particular laboratory's responsibility to validate an assembled NAT prior to using it for testing and reporting results on human samples. There are few published guidelines for the validation of assembled NAT. One procedure that laboratories can use to establish a validation process for an assay is detailed in this document. Before validating a method, laboratories must optimise it and then document the protocol. All instruments must be calibrated and maintained throughout the testing process. The validation process involves a series of steps including: (i) testing of dilution series of positive samples to determine the limits of detection of the assay and their linearity over concentrations to be measured in quantitative NAT; (ii) establishing the day-to-day variation of the assay's performance; (iii) evaluating the sensitivity and specificity of the assay as far as practicable, along with the extent of cross-reactivity with other genomic material; and (iv) assuring the quality of assembled assays using quality control procedures that monitor the performance of reagent batches before introducing new lots of reagent for testing.

Mitigating inconsistencies by coupling data cleaning, filtering, and contextual data validation in wireless sensor networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.

Mitigating Inconsistencies by Coupling Data Cleaning, Filtering, and Contextual Data Validation in Wireless Sensor Networks

With the advent of peer to peer networks, and more importantly sensor networks, the desire to extract useful information from continuous and unbounded streams of data has become more prominent. For example, in tele-health applications, sensor based data streaming systems are used to continuously and accurately monitor Alzheimer's patients and their surrounding environment. Typically, the requirements of such applications necessitate the cleaning and filtering of continuous, corrupted and incomplete data streams gathered wirelessly in dynamically varying conditions. Yet, existing data stream cleaning and filtering schemes are incapable of capturing the dynamics of the environment while simultaneously suppressing the losses and corruption introduced by uncertain environmental, hardware, and network conditions. Consequently, existing data cleaning and filtering paradigms are being challenged. This dissertation develops novel schemes for cleaning data streams received from a wireless sensor network operating under non-linear and dynamically varying conditions. The study establishes a paradigm for validating spatio-temporal associations among data sources to enhance data cleaning. To simplify the complexity of the validation process, the developed solution maps the requirements of the application on a geometrical space and identifies the potential sensor nodes of interest. Additionally, this dissertation models a wireless sensor network data reduction system by ascertaining that segregating data adaptation and prediction processes will augment the data reduction rates. The schemes presented in this study are evaluated using simulation and information theory concepts. The results demonstrate that dynamic conditions of the environment are better managed when validation is used for data cleaning. They also show that when a fast convergent adaptation process is deployed, data reduction rates are significantly improved. Targeted applications of the developed methodology include machine health monitoring, tele-health, environment and habitat monitoring, intermodal transportation and homeland security.

Développement et validation d’un questionnaire sur les connaissances en nutrition pour une population Canadienne-française

Plusieurs facteurs d’ordre environnemental, social ou individuel peuvent influencer l’adhésion à une saine alimentation. Parmi les déterminants individuels, les connaissances en nutrition ont un rôle à jouer. D’ailleurs, de nombreux programmes de promotion de la saine alimentation se basent sur une amélioration des connaissances en nutrition pour engendrer des changements positifs dans les comportements et les apports alimentaires de la population. Plusieurs études ont associé positivement le niveau de connaissances en nutrition et l’alimentation saine, mais il a été observé que les associations sont plus marquées avec l’utilisation de questionnaires validés sur les connaissances en nutrition. Une validation rigoureuse des questionnaires est donc primordiale pour assurer la validité des résultats obtenus. Au Canada, il n’existe pas d’outil spécifiquement conçu pour mesurer les connaissances en nutrition et plus précisément l’adhésion à la saine alimentation telle que présentée par le Guide alimentaire canadien (GAC), et ce mémoire illustre la pertinence d’un tel instrument pour la population canadienne. Les résultats de la validation ont mené à l’obtention d’un questionnaire valide et fiable pour la population à l’étude. L’usage de documents reconnus dans la littérature pour la conception du questionnaire de même que l’application de plusieurs méthodes de validation telles qu’utilisées par d’autres auteurs dans le domaine ont permis de bien valider l’instrument. Le questionnaire développé pourrait donc permettre la mesure adéquate des connaissances en nutrition dans un contexte canadien-français.

Measuring the stigma surrounding Type 2 diabetes: development and validation of the Type 2 Diabetes Stigma Assessment Scale (DSAS-2)

OBJECTIVE: To develop and validate a self-report measure of perceived and experienced stigma for use with adults with type 2 diabetes: the Type 2 Diabetes Stigma Assessment Scale (DSAS-2). RESEARCH DESIGN AND METHODS: An item pool was drafted based on qualitative data from 25 adults with type 2 diabetes and content from other health-related stigma questionnaires. Thirteen adults with type 2 diabetes completed 57 draft diabetes stigma items and participated in cognitive debriefing interviews. Based on participant feedback, the pool was reduced to 48 items with a 5-point Likert scale (strongly disagree to strongly agree). A total of 1,064 adults with type 2 diabetes completed a survey including these 48 items and other validated measures. Data were subject to principal components analysis and Spearman ρ correlations. RESULTS: The scale was reduced to 19 items, with an unforced three-factor solution indicative of three subscales: Treated Differently (6 items, α = 0.88), Blame and Judgment (7 items, α = 0.90), and Self-stigma (6 items, α = 0.90). A forced one-factor solution supported the calculation of a total score (α = 0.95). Satisfactory concurrent, convergent, and discriminant validity were demonstrated. CONCLUSIONS: The 19-item DSAS-2 is a reliable and valid measure of type 2 diabetes stigma. A rigorous design and validation process has resulted in a relatively brief measure of perceived and experienced stigma in type 2 diabetes. The novel scale has satisfactory psychometric properties and is now available to facilitate much-needed research in this field.

TestMEReq : generating abstract tests for requirements validation

This paper introduces TestMEReq, an automated tool for early validation of requirements. TestMEReq supports requirements engineers (REs) in the validation of the correctness, completeness and consistency of elicited requirements with minimum effort and time through generated abstract tests components: test requirements and test cases, and a mock-up prototype of the user interface (UI). Abstract tests are derived from abstract models called Essential Use Cases (EUCs) and the Essential User Interface (EUI). Our evaluation results show that TestMEReq is useful in the requirements validation process: it reduces the effort and time spent to ensure good quality requirements.

An experimental and finite element investigation of the biomechanics of vertebral compression fractures

Osteoporosis is a disease characterized by low bone mass and micro-architectural deterioration of bone tissue, with a consequent increase in bone fragility and susceptibility to fracture. Osteoporosis affects over 200 million people worldwide, with an estimated 1.5 million fractures annually in the United States alone, and with attendant costs exceeding $10 billion dollars per annum. Osteoporosis reduces bone density through a series of structural changes to the honeycomb-like trabecular bone structure (micro-structure). The reduced bone density, coupled with the microstructural changes, results in significant loss of bone strength and increased fracture risk. Vertebral compression fractures are the most common type of osteoporotic fracture and are associated with pain, increased thoracic curvature, reduced mobility, and difficulty with self care. Surgical interventions, such as kyphoplasty or vertebroplasty, are used to treat osteoporotic vertebral fractures by restoring vertebral stability and alleviating pain. These minimally invasive procedures involve injecting bone cement into the fractured vertebrae. The techniques are still relatively new and while initial results are promising, with the procedures relieving pain in 70-95% of cases, medium-term investigations are now indicating an increased risk of adjacent level fracture following the procedure. With the aging population, understanding and treatment of osteoporosis is an increasingly important public health issue in developed Western countries. The aim of this study was to investigate the biomechanics of spinal osteoporosis and osteoporotic vertebral compression fractures by developing multi-scale computational, Finite Element (FE) models of both healthy and osteoporotic vertebral bodies. The multi-scale approach included the overall vertebral body anatomy, as well as a detailed representation of the internal trabecular microstructure. This novel, multi-scale approach overcame limitations of previous investigations by allowing simultaneous investigation of the mechanics of the trabecular micro-structure as well as overall vertebral body mechanics. The models were used to simulate the progression of osteoporosis, the effect of different loading conditions on vertebral strength and stiffness, and the effects of vertebroplasty on vertebral and trabecular mechanics. The model development process began with the development of an individual trabecular strut model using 3D beam elements, which was used as the building block for lattice-type, structural trabecular bone models, which were in turn incorporated into the vertebral body models. At each stage of model development, model predictions were compared to analytical solutions and in-vitro data from existing literature. The incremental process provided confidence in the predictions of each model before incorporation into the overall vertebral body model. The trabecular bone model, vertebral body model and vertebroplasty models were validated against in-vitro data from a series of compression tests performed using human cadaveric vertebral bodies. Firstly, trabecular bone samples were acquired and morphological parameters for each sample were measured using high resolution micro-computed tomography (CT). Apparent mechanical properties for each sample were then determined using uni-axial compression tests. Bone tissue properties were inversely determined using voxel-based FE models based on the micro-CT data. Specimen specific trabecular bone models were developed and the predicted apparent stiffness and strength were compared to the experimentally measured apparent stiffness and strength of the corresponding specimen. Following the trabecular specimen tests, a series of 12 whole cadaveric vertebrae were then divided into treated and non-treated groups and vertebroplasty performed on the specimens of the treated group. The vertebrae in both groups underwent clinical-CT scanning and destructive uniaxial compression testing. Specimen specific FE vertebral body models were developed and the predicted mechanical response compared to the experimentally measured responses. The validation process demonstrated that the multi-scale FE models comprising a lattice network of beam elements were able to accurately capture the failure mechanics of trabecular bone; and a trabecular core represented with beam elements enclosed in a layer of shell elements to represent the cortical shell was able to adequately represent the failure mechanics of intact vertebral bodies with varying degrees of osteoporosis. Following model development and validation, the models were used to investigate the effects of progressive osteoporosis on vertebral body mechanics and trabecular bone mechanics. These simulations showed that overall failure of the osteoporotic vertebral body is initiated by failure of the trabecular core, and the failure mechanism of the trabeculae varies with the progression of osteoporosis; from tissue yield in healthy trabecular bone, to failure due to instability (buckling) in osteoporotic bone with its thinner trabecular struts. The mechanical response of the vertebral body under load is highly dependent on the ability of the endplates to deform to transmit the load to the underlying trabecular bone. The ability of the endplate to evenly transfer the load through the core diminishes with osteoporosis. Investigation into the effect of different loading conditions on the vertebral body found that, because the trabecular bone structural changes which occur in osteoporosis result in a structure that is highly aligned with the loading direction, the vertebral body is consequently less able to withstand non-uniform loading states such as occurs in forward flexion. Changes in vertebral body loading due to disc degeneration were simulated, but proved to have little effect on osteoporotic vertebra mechanics. Conversely, differences in vertebral body loading between simulated invivo (uniform endplate pressure) and in-vitro conditions (where the vertebral endplates are rigidly cemented) had a dramatic effect on the predicted vertebral mechanics. This investigation suggested that in-vitro loading using bone cement potting of both endplates has major limitations in its ability to represent vertebral body mechanics in-vivo. And lastly, FE investigation into the biomechanical effect of vertebroplasty was performed. The results of this investigation demonstrated that the effect of vertebroplasty on overall vertebra mechanics is strongly governed by the cement distribution achieved within the trabecular core. In agreement with a recent study, the models predicted that vertebroplasty cement distributions which do not form one continuous mass which contacts both endplates have little effect on vertebral body stiffness or strength. In summary, this work presents the development of a novel, multi-scale Finite Element model of the osteoporotic vertebral body, which provides a powerful new tool for investigating the mechanics of osteoporotic vertebral compression fractures at the trabecular bone micro-structural level, and at the vertebral body level.

Mining patterns and factors contributing to crash severity on road curves

Road curves are an important feature of road infrastructure and many serious crashes occur on road curves. In Queensland, the number of fatalities is twice as many on curves as that on straight roads. Therefore, there is a need to reduce drivers’ exposure to crash risk on road curves. Road crashes in Australia and in the Organisation for Economic Co-operation and Development(OECD) have plateaued in the last five years (2004 to 2008) and the road safety community is desperately seeking innovative interventions to reduce the number of crashes. However, designing an innovative and effective intervention may prove to be difficult as it relies on providing theoretical foundation, coherence, understanding, and structure to both the design and validation of the efficiency of the new intervention. Researchers from multiple disciplines have developed various models to determine the contributing factors for crashes on road curves with a view towards reducing the crash rate. However, most of the existing methods are based on statistical analysis of contributing factors described in government crash reports. In order to further explore the contributing factors related to crashes on road curves, this thesis designs a novel method to analyse and validate these contributing factors. The use of crash claim reports from an insurance company is proposed for analysis using data mining techniques. To the best of our knowledge, this is the first attempt to use data mining techniques to analyse crashes on road curves. Text mining technique is employed as the reports consist of thousands of textual descriptions and hence, text mining is able to identify the contributing factors. Besides identifying the contributing factors, limited studies to date have investigated the relationships between these factors, especially for crashes on road curves. Thus, this study proposed the use of the rough set analysis technique to determine these relationships. The results from this analysis are used to assess the effect of these contributing factors on crash severity. The findings obtained through the use of data mining techniques presented in this thesis, have been found to be consistent with existing identified contributing factors. Furthermore, this thesis has identified new contributing factors towards crashes and the relationships between them. A significant pattern related with crash severity is the time of the day where severe road crashes occur more frequently in the evening or night time. Tree collision is another common pattern where crashes that occur in the morning and involves hitting a tree are likely to have a higher crash severity. Another factor that influences crash severity is the age of the driver. Most age groups face a high crash severity except for drivers between 60 and 100 years old, who have the lowest crash severity. The significant relationship identified between contributing factors consists of the time of the crash, the manufactured year of the vehicle, the age of the driver and hitting a tree. Having identified new contributing factors and relationships, a validation process is carried out using a traffic simulator in order to determine their accuracy. The validation process indicates that the results are accurate. This demonstrates that data mining techniques are a powerful tool in road safety research, and can be usefully applied within the Intelligent Transport System (ITS) domain. The research presented in this thesis provides an insight into the complexity of crashes on road curves. The findings of this research have important implications for both practitioners and academics. For road safety practitioners, the results from this research illustrate practical benefits for the design of interventions for road curves that will potentially help in decreasing related injuries and fatalities. For academics, this research opens up a new research methodology to assess crash severity, related to road crashes on curves.

Numerical simulation of axially loaded concrete columns under transverse impact and vulnerability assessment

With a view to assessing the vulnerability of columns to low elevation vehicular impacts, a non-linear explicit numerical model has been developed and validated using existing experimental results. The numerical model accounts for the effects of strain rate and confinement of the reinforced concrete, which are fundamental to the successful prediction of the impact response. The sensitivity of the material model parameters used for the validation is also scrutinised and numerical tests are performed to examine their suitability to simulate the shear failure conditions. Conflicting views on the strain gradient effects are discussed and the validation process is extended to investigate the ability of the equations developed under concentric loading conditions to simulate flexural failure events. Experimental data on impact force–time histories, mid span and residual deflections and support reactions have been verified against corresponding numerical results. A universal technique which can be applied to determine the vulnerability of the impacted columns against collisions with new generation vehicles under the most common impact modes is proposed. Additionally, the observed failure characteristics of the impacted columns are explained using extended outcomes. Based on the overall results, an analytical method is suggested to quantify the vulnerability of the columns.

Safety in occupational driving : development of a driver behaviour scale for the workplace context

International statistics indicate that occupational, or work-related driving, crashes are the most common cause of workplace injury, death, and absence from work. The majority of research examining unsafe driver behavior in the workplace has relied on general road safety questionnaires. However, past research has failed to consider the organizational context in the use of these questionnaires, and as such, there is ambiguity in the dimensions constituting occupational driving. Using a theoretical model developed by Hockey (1993, 1997), this article proposes and validates a new scale of occupational driver behavior. This scale incorporates four dimensions of driver behavior that are influenced by demanding workplace conditions; speeding, rule violation, inattention, and driving while tired. Following a content validation process, three samples of occupational drivers in Australia were used to assess the scale. Data from the first sample (n=145) were used to reduce the number of scale items and provide an assessment of the factorial validity of the scale. Data from the second sample (n=645) were then used to confirm the factor structure and psychometric properties of the scale including reliability and construct validity. Finally, data from the third sample (n=248) were used to establish criterion validity. The results indicated that the scale is a reliable and valid measure of occupational driver behavior.

Vulnerability assessment of reinforced concrete columns subjected to vehicular impacts

Columns are one of the key load bearing elements that are highly susceptible to vehicle impacts. The resulting severe damages to columns may leads to failures of the supporting structure that are catastrophic in nature. However, the columns in existing structures are seldom designed for impact due to inadequacies of design guidelines. The impact behaviour of columns designed for gravity loads and actions other than impact is, therefore, of an interest. A comprehensive investigation is conducted on reinforced concrete column with a particular focus on investigating the vulnerability of the exposed columns and to implement mitigation techniques under low to medium velocity car and truck impacts. The investigation is based on non-linear explicit computer simulations of impacted columns followed by a comprehensive validation process. The impact is simulated using force pulses generated from full scale vehicle impact tests. A material model capable of simulating triaxial loading conditions is used in the analyses. Circular columns adequate in capacity for five to twenty story buildings, designed according to Australian standards are considered in the investigation. The crucial parameters associated with the routine column designs and the different load combinations applied at the serviceability stage on the typical columns are considered in detail. Axially loaded columns are examined at the initial stage and the investigation is extended to analyse the impact behaviour under single axis bending and biaxial bending. The impact capacity reduction under varying axial loads is also investigated. Effects of the various load combinations are quantified and residual capacity of the impacted columns based on the status of the damage and mitigation techniques are also presented. In addition, the contribution of the individual parameter to the failure load is scrutinized and analytical equations are developed to identify the critical impulses in terms of the geometrical and material properties of the impacted column. In particular, an innovative technique was developed and introduced to improve the accuracy of the equations where the other techniques are failed due to the shape of the error distribution. Above all, the equations can be used to quantify the critical impulse for three consecutive points (load combinations) located on the interaction diagram for one particular column. Consequently, linear interpolation can be used to quantify the critical impulse for the loading points that are located in-between on the interaction diagram. Having provided a known force and impulse pair for an average impact duration, this method can be extended to assess the vulnerability of columns for a general vehicle population based on an analytical method that can be used to quantify the critical peak forces under different impact durations. Therefore the contribution of this research is not only limited to produce simplified yet rational design guidelines and equations, but also provides a comprehensive solution to quantify the impact capacity while delivering new insight to the scientific community for dealing with impacts.

Validating the is-impact model in the Malaysian public sector

This research is one of several ongoing studies conducted within the IT Professional Services (ITPS) research programme at Queensland University of Technology (QUT). In 2003, ITPS introduced the IS-Impact model, a measurement model for measuring information systems success from the viewpoint of multiple stakeholders. The model, along with its instrument, is robust, simple, yet generalisable, and yields results that are comparable across time, stakeholders, different systems and system contexts. The IS-Impact model is defined as “a measure at a point in time, of the stream of net benefits from the Information System (IS), to date and anticipated, as perceived by all key-user-groups”. The model represents four dimensions, which are ‘Individual Impact’, ‘Organizational Impact’, ‘Information Quality’ and ‘System Quality’. The two Impact dimensions measure the up-to-date impact of the evaluated system, while the remaining two Quality dimensions act as proxies for probable future impacts (Gable, Sedera & Chan, 2008). To fulfil the goal of ITPS, “to develop the most widely employed model” this research re-validates and extends the IS-Impact model in a new context. This method/context-extension research aims to test the generalisability of the model by addressing known limitations of the model. One of the limitations of the model relates to the extent of external validity of the model. In order to gain wide acceptance, a model should be consistent and work well in different contexts. The IS-Impact model, however, was only validated in the Australian context, and packaged software was chosen as the IS understudy. Thus, this study is concerned with whether the model can be applied in another different context. Aiming for a robust and standardised measurement model that can be used across different contexts, this research re-validates and extends the IS-Impact model and its instrument to public sector organisations in Malaysia. The overarching research question (managerial question) of this research is “How can public sector organisations in Malaysia measure the impact of information systems systematically and effectively?” With two main objectives, the managerial question is broken down into two specific research questions. The first research question addresses the applicability (relevance) of the dimensions and measures of the IS-Impact model in the Malaysian context. Moreover, this research question addresses the completeness of the model in the new context. Initially, this research assumes that the dimensions and measures of the IS-Impact model are sufficient for the new context. However, some IS researchers suggest that the selection of measures needs to be done purposely for different contextual settings (DeLone & McLean, 1992, Rai, Lang & Welker, 2002). Thus, the first research question is as follows, “Is the IS-Impact model complete for measuring the impact of IS in Malaysian public sector organisations?” [RQ1]. The IS-Impact model is a multidimensional model that consists of four dimensions or constructs. Each dimension is represented by formative measures or indicators. Formative measures are known as composite variables because these measures make up or form the construct, or, in this case, the dimension in the IS-Impact model. These formative measures define different aspects of the dimension, thus, a measurement model of this kind needs to be tested not just on the structural relationship between the constructs but also the validity of each measure. In a previous study, the IS-Impact model was validated using formative validation techniques, as proposed in the literature (i.e., Diamantopoulos and Winklhofer, 2001, Diamantopoulos and Siguaw, 2006, Petter, Straub and Rai, 2007). However, there is potential for improving the validation testing of the model by adding more criterion or dependent variables. This includes identifying a consequence of the IS-Impact construct for the purpose of validation. Moreover, a different approach is employed in this research, whereby the validity of the model is tested using the Partial Least Squares (PLS) method, a component-based structural equation modelling (SEM) technique. Thus, the second research question addresses the construct validation of the IS-Impact model; “Is the IS-Impact model valid as a multidimensional formative construct?” [RQ2]. This study employs two rounds of surveys, each having a different and specific aim. The first is qualitative and exploratory, aiming to investigate the applicability and sufficiency of the IS-Impact dimensions and measures in the new context. This survey was conducted in a state government in Malaysia. A total of 77 valid responses were received, yielding 278 impact statements. The results from the qualitative analysis demonstrate the applicability of most of the IS-Impact measures. The analysis also shows a significant new measure having emerged from the context. This new measure was added as one of the System Quality measures. The second survey is a quantitative survey that aims to operationalise the measures identified from the qualitative analysis and rigorously validate the model. This survey was conducted in four state governments (including the state government that was involved in the first survey). A total of 254 valid responses were used in the data analysis. Data was analysed using structural equation modelling techniques, following the guidelines for formative construct validation, to test the validity and reliability of the constructs in the model. This study is the first research that extends the complete IS-Impact model in a new context that is different in terms of nationality, language and the type of information system (IS). The main contribution of this research is to present a comprehensive, up-to-date IS-Impact model, which has been validated in the new context. The study has accomplished its purpose of testing the generalisability of the IS-Impact model and continuing the IS evaluation research by extending it in the Malaysian context. A further contribution is a validated Malaysian language IS-Impact measurement instrument. It is hoped that the validated Malaysian IS-Impact instrument will encourage related IS research in Malaysia, and that the demonstrated model validity and generalisability will encourage a cumulative tradition of research previously not possible. The study entailed several methodological improvements on prior work, including: (1) new criterion measures for the overall IS-Impact construct employed in ‘identification through measurement relations’; (2) a stronger, multi-item ‘Satisfaction’ construct, employed in ‘identification through structural relations’; (3) an alternative version of the main survey instrument in which items are randomized (rather than blocked) for comparison with the main survey data, in attention to possible common method variance (no significant differences between these two survey instruments were observed); (4) demonstrates a validation process of formative indexes of a multidimensional, second-order construct (existing examples mostly involved unidimensional constructs); (5) testing the presence of suppressor effects that influence the significance of some measures and dimensions in the model; and (6) demonstrates the effect of an imbalanced number of measures within a construct to the contribution power of each dimension in a multidimensional model.

Validity and reliability of instruments designed to measure factors influencing the overuse of antibiotics

Background: Antibiotic overuse is a global public health issue that is influenced by several factors. The degree and prevalence of antibiotic overuse is difficult to measure directly. A more practical approach, such as the use of a psycho-social measurement instrument, might allow for the observation and assessment of patterns of antibiotic use. Study objective: The aim of this paper is to review the nature, validity, and reliability of measurement scales designed to measure factors associated with antibiotic misuse/overuse. Design: This study is descriptive and includes a systematic integration of the measurement scales used in the literature to measure factors associated with antibiotic misuse/overuse. The review included 70 international scientific publications from 1992 to 2010. Main results: Studies have presented scales to measure antibiotic misuse. However, the workup of these instruments is often not mentioned, or the scales are used with only early-phase validation, such as content or face validity. Other studies have discussed the reliability of these scales. However, the full validation process has not been discussed in any of the reviewed measurement scales. Conclusion: A reliable, fully validated measurement scale must be developed to assess the factors associated with the overuse of antibiotics. Identifying these factors will help to minimize the misuse of antibiotics.

Reliability of partial ambiguity fixing with multiple GNSS constellations

Reliable ambiguity resolution (AR) is essential to Real-Time Kinematic (RTK) positioning and its applications, since incorrect ambiguity fixing can lead to largely biased positioning solutions. A partial ambiguity fixing technique is developed to improve the reliability of AR, involving partial ambiguity decorrelation (PAD) and partial ambiguity resolution (PAR). Decorrelation transformation could substantially amplify the biases in the phase measurements. The purpose of PAD is to find the optimum trade-off between decorrelation and worst-case bias amplification. The concept of PAR refers to the case where only a subset of the ambiguities can be fixed correctly to their integers in the integer least-squares (ILS) estimation system at high success rates. As a result, RTK solutions can be derived from these integer-fixed phase measurements. This is meaningful provided that the number of reliably resolved phase measurements is sufficiently large for least-square estimation of RTK solutions as well. Considering the GPS constellation alone, partially fixed measurements are often insufficient for positioning. The AR reliability is usually characterised by the AR success rate. In this contribution an AR validation decision matrix is firstly introduced to understand the impact of success rate. Moreover the AR risk probability is included into a more complete evaluation of the AR reliability. We use 16 ambiguity variance-covariance matrices with different levels of success rate to analyse the relation between success rate and AR risk probability. Next, the paper examines during the PAD process, how a bias in one measurement is propagated and amplified onto many others, leading to more than one wrong integer and to affect the success probability. Furthermore, the paper proposes a partial ambiguity fixing procedure with a predefined success rate criterion and ratio-test in the ambiguity validation process. In this paper, the Galileo constellation data is tested with simulated observations. Numerical results from our experiment clearly demonstrate that only when the computed success rate is very high, the AR validation can provide decisions about the correctness of AR which are close to real world, with both low AR risk and false alarm probabilities. The results also indicate that the PAR procedure can automatically chose adequate number of ambiguities to fix at given high-success rate from the multiple constellations instead of fixing all the ambiguities. This is a benefit that multiple GNSS constellations can offer.