Community pharmacy dispensing of prescription medicine sample packs: Changing the business of medicine initiation?

Prescription medicine samples (or starter packs) are provided by pharmaceutical manufacturers to prescribing doctors as one component in the suite of marketing products used to convince them to prescribe a particular medicine [1,2]. Samples are generally newer, more expensive treatment options still covered by patent [3,4]. Safe, effective, judicious and appropriate medicine use (quality use of medicines) [5] could be enhanced by involving community pharmacists in the dispensing of starter packs. Doctors who use samples show a trend towards prescribing more expensive medicines overall [6] and also prescribe more medicines [7]. Cardiovascular health and mental health are Australian National Health Priority Areas [8] and account for approximately 30% and 17%, respectively, of annual government Pharmaceutical Benefits System (PBS) in 2006 [9]. The PBS is Australia's universal prescription subsidy scheme [9]. Antihypertensives were a major contributor to the estimated 80 000 medicine-related hospital admissions in Australia in 1999 [10] and also internationally [11,12]. The aim of this study was to pilot an alternative model for supply of free sample or starter packs of prescription medicines and ascertain if it is a viable model in daily practice.

Exploitation of the menshutkin reaction for the controlled assembly of halogen bonded architectures incorporating 1,2-diiodotetrafluorobenzene and 1,3,5-Triiodotrifluorobenzene

1,4-Diazabicyclo[2.2.2]octane (DABCO) forms well-defined co-crystals with 1,2-diiodotetrafluorobenzene (1,2-DITFB), [(1,2-DITFB)2DABCO], and 1,3,5-triiodotrifluorobenzene, [(1,3,5-TITFB)2DABCO]. Both systems exhibited lower-than-expected supramolecular connectivity, which inspired a search for polymorphs in alternative crystallization solvents. In dichloromethane solution, the Menshutkin reaction was found to occur, generating chloride anions and quaternary ammonium cations through the reaction between the solvent and DABCO. The controlled in situ production of chloride ions facilitated the crystallization of new halogen bonded networks, DABCO–CH2Cl[(1,2-DITFB)Cl] (zigzag X-bonded chains) and (DABCO–CH2Cl)3[(1,3,5-TITFB)2Cl3]·CHCl3 (2D pseudo-trigonal X-bonded nets displaying Borremean entanglement), propagating with charge-assisted C–I···Cl– halogen bonds. The method was found to be versatile, and substitution of DABCO with triethylamine (TEA) gave (TEA-CH2Cl)3[(1,2-DITFB)Cl3]·4(H2O) (mixed halogen bond hydrogen bond network with 2D supramolecular connectivity) and TEA-CH2Cl[(1,3,5-TITFB)Cl] (tightly packed planar trigonal nets). The co-crystals were typically produced in high yield and purity with relatively predictable supramolecular topology, particularly with respect to the connectivity of the iodobenzene molecules. The potential to use this synthetic methodology for crystal engineering of halogen bonded architectures is demonstrated and discussed.

Maternal concern about her toddler undereating and becoming underweight and use of feeding practices - analysis of the NOURISH and SAIDI cohorts using structural equation modeling

The chubby baby who eats well is desirable in our culture. Perceived low weight gains and feeding concerns are common reasons mothers seek advice in the early years. In contrast, childhood obesity is a global public health concern. Use of coercive feeding practices, prompted by maternal concern about weight, may disrupt a child’s innate self regulation of energy intake, promoting overeating and overweight. This study describes predictors of maternal concern about her child undereating/becoming underweight and feeding practices. Mothers in the control group of the NOURISH and South Australian Infants Dietary Intake studies (n = 332) completed a self-administered questionnaire when the child was aged 12–16 months. Weight-for-age z-score (WAZ)was derived from weight measured by study staff. Mean age (SD) was 13.8 (1.3) months, mean WAZ (SD), 0.58 (0.86) and 49% were male. WAZ and two questions describing food refusal were combined in a structural equation model with four items from the Infant feeding Questionnaire (IFQ) to form the factor ‘Concern about undereating/weight’. Structural relationships were drawn between concern and IFQ factors ‘awareness of infant’s hunger and satiety cues’, ‘use of food to calm infant’s fussiness’ and ‘feeding infant on a schedule’, resulting in a model of acceptable fit. Lower WAZ and higher frequency of food refusal predicted higher maternal concern. Higher maternal concern was associated with lower awareness of infant cues (r = −.17, p = .01) and greater use of food to calm (r = .13, p = .03). In a cohort of healthy children, maternal concern about undereating and underweight was associated with practices that have the potential to disrupt self-regulation.

The waves in business transformation management: a conceptualization of how IT-enabled Business Transformations Unfold

We examine IT-enabled Business Transformations (ITBT) based on three case studies of successful, multi-year ERP implementation programs. Given the inconsistencies in segmenting the different key periods in ITBTs in both literature and our cases, we sought to consolidate the common events or critical incidents in such initiatives. We label those key periods as waves, and the emergence of triggers and reactions thereunto in the management of business transformations. We show that business transformations unfold in four distinct waves: Wave 1 Concept Development, Wave 2 Blueprint Design, Wave 3 Solution Delivery and Wave 4 Post-Transformation. These waves are characterized by the occurrence of strategic- and program-level triggers to which organizations respond by invoking different management services. Our interpretive research provides a new conceptualization of ITBTs based on a service-oriented view of such initiatives. This view draws attention to managerial capabilities as a service to transformations, and how and when these capabilities are required to respond to triggering incidents. We outline propositions and recommendations for business transformation management.

Standard business reporting in Australia : efficiency, effectiveness, or both?

The benefits of using eXtensible Business Reporting Language (XBRL) as a business reporting standard have been widely canvassed in the extant literature, in particular, as the enabling technology for standard business reporting tools. One of the key benefits noted is the ability of standard business reporting to create significant efficiencies in the regulatory reporting process. Efficiency-driven cost reductions are highly desirable by data and report producers. However, they may not have the same potential to create long-term firm value as improved effectiveness of decision making. This study assesses the perceptions of Australian business stakeholders in relation to the benefits of the Australian standard business reporting instantiation (SBR) for financial reporting. These perceptions were drawn from interviews of persons knowledgeable in XBRL-based standard business reporting and submissions to Treasury relative to SBR reporting options. The combination of interviews and submissions permit insights into the views of various groups of stakeholders in relation to the potential benefits. In line with predictions based on a transaction-cost economics perspective, interviewees who primarily came from a data and report-producer background mentioned benefits that centre largely on asset specificity and efficiency. The interviewees who principally came from a data and report-consumer background mentioned benefits that centre on reducing decision-making uncertainty and decision-making effectiveness. The data and report consumers also took a broader view of the benefits of SBR to the financial reporting supply chain. Our research suggests that advocates of SBR have successfully promoted its efficiency benefits to potential users. However, the effectiveness benefits of SBR, for example, the decision-making benefits offered to investors via standardised reports, while becoming more broadly acknowledged, remain not a priority for all stakeholders.

Detecting approximate clones in business process model repositories

Empirical evidence shows that repositories of business process models used in industrial practice contain significant amounts of duplication. This duplication arises for example when the repository covers multiple variants of the same processes or due to copy-pasting. Previous work has addressed the problem of efficiently retrieving exact clones that can be refactored into shared subprocess models. This article studies the broader problem of approximate clone detection in process models. The article proposes techniques for detecting clusters of approximate clones based on two well-known clustering algorithms: DBSCAN and Hi- erarchical Agglomerative Clustering (HAC). The article also defines a measure of standardizability of an approximate clone cluster, meaning the potential benefit of replacing the approximate clones with a single standardized subprocess. Experiments show that both techniques, in conjunction with the proposed standardizability measure, accurately retrieve clusters of approximate clones that originate from copy-pasting followed by independent modifications to the copied fragments. Additional experiments show that both techniques produce clusters that match those produced by human subjects and that are perceived to be standardizable.

A tag-based personalized item recommendation system using tensor modeling and topic model approaches

This research falls in the area of enhancing the quality of tag-based item recommendation systems. It aims to achieve this by employing a multi-dimensional user profile approach and by analyzing the semantic aspects of tags. Tag-based recommender systems have two characteristics that need to be carefully studied in order to build a reliable system. Firstly, the multi-dimensional correlation, called as tag assignment , should be appropriately modelled in order to create the user profiles [1]. Secondly, the semantics behind the tags should be considered properly as the flexibility with their design can cause semantic problems such as synonymy and polysemy [2]. This research proposes to address these two challenges for building a tag-based item recommendation system by employing tensor modeling as the multi-dimensional user profile approach, and the topic model as the semantic analysis approach. The first objective is to optimize the tensor model reconstruction and to improve the model performance in generating quality rec-ommendation. A novel Tensor-based Recommendation using Probabilistic Ranking (TRPR) method [3] has been developed. Results show this method to be scalable for large datasets and outperforming the benchmarking methods in terms of accuracy. The memory efficient loop implements the n-mode block-striped (matrix) product for tensor reconstruction as an approximation of the initial tensor. The probabilistic ranking calculates the probabil-ity of users to select candidate items using their tag preference list based on the entries generated from the reconstructed tensor. The second objective is to analyse the tag semantics and utilize the outcome in building the tensor model. This research proposes to investigate the problem using topic model approach to keep the tags nature as the “social vocabulary” [4]. For the tag assignment data, topics can be generated from the occurrences of tags given for an item. However there is only limited amount of tags availa-ble to represent items as collection of topics, since an item might have only been tagged by using several tags. Consequently, the generated topics might not able to represent the items appropriately. Furthermore, given that each tag can belong to any topics with various probability scores, the occurrence of tags cannot simply be mapped by the topics to build the tensor model. A standard weighting technique will not appropriately calculate the value of tagging activity since it will define the context of an item using a tag instead of a topic.

Empirical vehicle-to-vehicle pathloss modeling in highway, suburban and urban environments at 5.8GHz

In this paper, we present a pathloss characterization for vehicle-to-vehicle (V2V) communications based on empirical data collected from extensive measurement campaign performed under line-of-sight (LOS), non-line-of-sight (NLOS) and varying traffic densities. The experiment was conducted in three different V2V propagation environments: highway, suburban and urban at 5.8GHz. We developed pathloss models for each of the three different V2V environments considered. Based on a log-distance power law model, the values for the pathloss exponent and the standard deviation of shadowing were reported. The average pathloss exponent ranges from 1.77 for highway, 1.68 for the urban to 1.53 for the suburban environment. The reported results can contribute to vehicular network (VANET) simulators and can be used by system designers to develop, evaluate and validate new protocols and system designs under realistic propagation conditions.

How small business advisory program delivery methods (collective learning, tailored, and practice-based approaches) affect learning and innovation

Past studies relate small business advisory program effectiveness to advisory characteristics such as advisory intensity and scope. We contribute to existing literature by seeking to identify the impact of different advisory program methods of delivery on learning and subsequent firm innovation behavior. Our research is based on a survey of 257 Australian firms completing small business advisory programs in the three years preceding the research. We explore the range of small business advisory program delivery methods in which our surveyed firms participated and, with reference to the literature on organizational learning and innovation, we analyze predictors of firms' learning ability and innovativeness based on the identified delivery methods. First, we found that business advisory programs that involved high levels of collective learning and tailored approaches enhanced firms' perceptions of their learning of critical skills or capabilities. We also found that small business advisory programs that were delivered by using practice-based approaches enhanced firms' subsequent organizational innovation. We verified this finding by testing whether firms that have participated in small business advisory services subsequently demonstrate improved behavior in terms of organizational innovativeness, when compared with matched firms that have not participated in an advisory program.

Component modeling for SCADA network mapping

Supervisory Control and Data Acquisition systems (SCADA) are widely used to control critical infrastructure automatically. Capturing and analyzing packet-level traffic flowing through such a network is an essential requirement for problems such as legacy network mapping and fault detection. Within the framework of captured network traffic, we present a simple modeling technique, which supports the mapping of the SCADA network topology via traffic monitoring. By characterizing atomic network components in terms of their input-output topology and the relationship between their data traffic logs, we show that these modeling primitives have good compositional behaviour, which allows complex networks to be modeled. Finally, the predictions generated by our model are found to be in good agreement with experimentally obtained traffic.

Adsorption thermodynamics and kinetics study for the self-assembly of 1,8,15,22-tetraaminophthalocyanatocobalt(II) on glassy carbon surface

Spontaneous adsorption of 1,8,15,22-tetraaminophthalocyanatocobalt(II) (4α-CoIITAPc) on glassy carbon (GC) electrode leads to the formation of a stable self-assembled monolayer (SAM). Since the SAM of 4α-CoIITAPc is redox active, its adsorption on GC electrode was followed by cyclic voltammetry. SAM of 4α-CoIITAPc on GC electrode shows two pairs of well-defined redox peaks corresponding to CoIII/CoII and CoIIIPc−1/CoIIIPc−2. The surface coverage (Γ) value, calculated by integrating the charge under CoII oxidation, was used to study the adsorption thermodynamics and kinetics of 4α-CoIITAPc on GC surface. Cyclic voltammetric studies show that the adsorption of 4α-CoIITAPc on GC electrode has reached the saturation coverage (Γs) within 3 h. The Γs value for the SAM of 4α-CoIITAPc on GC electrode was found to be 2.37 × 10−10 mol cm−2. Gibbs free energy (ΔGads) and adsorption rate constant (kad) for the adsorption of 4α-CoIITAPc on GC surface were found to be −16.76 kJ mol−1 and 7.1 M−1 s−1, respectively. The possible mechanism for the self-assembly of 4α-CoIITAPc on GC surface is through the addition of nucleophilic amines to the olefinic bond on the GC surface in addition to a meager contribution from π stacking. The contribution of π stacking was confirmed from the adsorption of unsubstituted phthalocyanatocobalt(II) (CoPc) on GC electrode. Raman spectra for the SAM of 4α-CoIITAPc on carbon surface shows strong stretching and breathing bands of Pc macrocycle, pyrrole ring and isoindole ring. Raman and CV studies suggest that 4α-CoIITAPc is adopting nearly a flat orientation or little bit tilted orientation.

An interpretative structural modeling based network reconfiguration strategy for power systems

The network reconfiguration is an important stage of restoring a power system after a complete blackout or a local outage. Reasonable planning of the network reconfiguration procedure is essential for rapidly restoring the power system concerned. An approach for evaluating the importance of a line is first proposed based on the line contraction concept. Then, the interpretative structural modeling (ISM) is employed to analyze the relationship among the factors having impacts on the network reconfiguration. The security and speediness of restoring generating units are considered with priority, and a method is next proposed to select the generating unit to be restored by maximizing the restoration benefit with both the generation capacity of the restored generating unit and the importance of the line in the restoration path considered. Both the start-up sequence of generating units and the related restoration paths are optimized together in the proposed method, and in this way the shortcomings of separately solving these two issues in the existing methods are avoided. Finally, the New England 10-unit 39-bus power system and the Guangdong power system in South China are employed to demonstrate the basic features of the proposed method.

A critical review of structural equation modeling applications in construction research

Structural equation modeling (SEM) is a versatile multivariate statistical technique, and applications have been increasing since its introduction in the 1980s. This paper provides a critical review of 84 articles involving the use of SEM to address construction related problems over the period 1998–2012 including, but not limited to, seven top construction research journals. After conducting a yearly publication trend analysis, it is found that SEM applications have been accelerating over time. However, there are inconsistencies in the various recorded applications and several recurring problems exist. The important issues that need to be considered are examined in research design, model development and model evaluation and are discussed in detail with reference to current applications. A particularly important issue concerns the construct validity. Relevant topics for efficient research design also include longitudinal or cross-sectional studies, mediation and moderation effects, sample size issues and software selection. A guideline framework is provided to help future researchers in construction SEM applications.

Clinical data warehousing : a business analytics approach for managing health data

Heterogeneous health data is a critical issue when managing health information for quality decision making processes. In this paper we examine the efficient aggregation of lifestyle information through a data warehousing architecture lens. We present a proof of concept for a clinical data warehouse architecture that enables evidence based decision making processes by integrating and organising disparate data silos in support of healthcare services improvement paradigms.

Exploring cognitive style and task-specific preferences for process representations

Process models describe someone’s understanding of processes. Processes can be described using unstructured, semi-formal or diagrammatic representation forms. These representations are used in a variety of task settings, ranging from understanding processes to executing or improving processes, with the implicit assumption that the chosen representation form will be appropriate for all task settings. We explore the validity of this assumption by examining empirically the preference for different process representation forms depending on the task setting and cognitive style of the user. Based on data collected from 120 business school students, we show that preferences for process representation formats vary dependent on application purpose and cognitive styles of the participants. However, users consistently prefer diagrams over other representation formats. Our research informs a broader research agenda on task-specific applications of process modeling. We offer several recommendations for further research in this area.