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Prescribers’ views and experiences of assessing the appropriateness of prescribed medications in a specialist addiction service
No full textBackground Mental and physical health problems are common in people with substance misuse problems and medications are often required in their management. Given the extent of prescribing for service users who attend specialist addiction services, it is important to consider how prescribers in this setting assess the appropriateness of service users’ prescribed medications. Objective To explore prescribers’ views and experiences of assessing the appropriateness of medications prescribed for service users coming in for treatment as well as the differences between prescribers. Setting A specialist addiction service in the North of England. Method A phenomenological approach was adopted. Semi-structured interviews were conducted with four nurse prescribers and eight doctors. Data were analysed using thematic framework analysis. Main outcome measure Prescribers’ views and experiences of assessing the appropriateness of prescribed medications. Results Assessment of the appropriateness of prescribed medications involved reviewing medications, assessing risk, history-taking, involvement of service users, and comparing guideline adherence and ‘successful’ prescribing. Doctors and nurse prescribers assessed the appropriateness of medications they considered to be within their competency. Doctors provided support to nurse prescribers and general practitioners (GPs) when dealing with issues around prescribing. Conclusion Assessment of the appropriateness of prescribed medications is complex. The recent reduction in medical expertise in specialist addiction services may negatively impact on the clinical management of service users. It appears that there is a need for further training of nurse prescribers and GPs so they can provide optimal care to service users
2017 Teaching and Learning Conference “Building an Academic community: engaging our students”
No full textThe 2017 Teaching and Learning Conference “Building an Academic community: engaging our students” at the University of Huddersfield brought together over 200 delegates from across the seven schools and services, to consider approaches that would support the development of an inclusive, high achieving academic community. The morning started with an overview of the University’s achievements and successes in Teaching and Learning since the 2016 conference “Bridging the Gaps: redefining excellence in Teaching and Learning”. The reflective summary noted the prestigious Gold Award in the recent Teaching Excellence Framework, our unbroken succession of National Teaching Fellowships, which spans ten years; the strong relationships between research and teaching and the University’s excellent employability record, which demonstrates the relevance of our teaching to industry, commerce, the public sector and the community. Colleagues, students and the wider network of partners and collaborators were thanked for their contribution, enthusiasm, and commitment to excellence and innovation in Learning and Teaching. The most recent acknowledgement from the sector in testimony to Huddersfield’s Excellence in Teaching and Learning (T&L) was received the week prior to the conference, when the University won the Higher Education Academy’s inaugural Global Teaching Excellence Award, beating 26 finalists from across the world
The Need For Lifelong Learning In Nigeria’s Banking Industry
No full textThe continuous change in every facet of today’s world has created the need for every individual and organisation to engage with Lifelong Learning, and has further underscored the age-long argument that learning is from cradle to grave; from birth to death. The need for Lifelong Learning in the workplace has particularly proved germane given the knowledge-driven world we now live in. Amongst all the workplaces of the world, the banking industry appears to be very important and strategic, in terms of national financial issues and development. Given this role, for the banking industry of a developing economy like Nigeria’s, the desire for Lifelong Learning cannot be overrated and overemphasised. This article is a position paper which identifies gaps in skills in the Nigerian banking industry and highlights the importance of Lifelong Learning in this regard. The paper provides specific recommendations on how Lifelong Learning can be embedded in the Nigerian banking industry
Teacher Educator and Teachers in Training: A Case-Study Charting the Development of Professional Identities
No full textAs a New Teacher Educator (NTE) within Further Education (FE), professional identity was brought abruptly into my consciousness as I scrutinised and even criticised my practice. The weight of responsibility for supporting a diverse group of trainees through their Initial Teacher Training (ITT) programme was not without anxiety and self-doubt. A number of challenges presented that required careful management on my part. This paper charts the complexity of the development of new professional identities within one ITT classroom and how collaborative enquiry was used effectively to build a supportive environment that nurtured both my transition from teacher to Teacher Educator and the trainees’ transitions from teachers in training to qualified teachers
Young migrants’ narratives of collective identifications and belonging
No full textThe article sheds light on the intricate relationship between migration, ‘identity’ and belonging by focusing on young migrants in the context of Greek society. Based upon a qualitative study of youth identities, the key objective is to examine their collective identifications, formed through the dialectic of self-identification and categorization. The analysis of young migrants’ narratives unpacks how their sense of belonging and emotional attachments to their countries of origin and
settlement are mediated by processes of racialization and ‘othering’
Cyber-Bullying And Children’s Unmonitored Media Violence Exposure
Get PDFWith technological evolution, interpersonal communication is constantly advancing; as a result comes the more frequent unregulated access of children to cyber-space and media violence exposure (DePaolis, & Williford, 2015), whilst risking involvement to cyber-bullying (CB). CB is commonly defined as purposefully causing repetitively harm to others through electronic devices created for interpersonal communication (Rigby, 2002). Its main differentiation from traditional bullying is the perpetrator’s ability to anonymously and effortless harass multiple victims at any time and geographic location (Hemphill, Tollit, Kotevski & Heerde, 2015). Research (for example see Mishna, Cook, Gadalla, Daciuk & Solomon, 2010) has indicated CB rates of up to 49.5% for cyber-victimisation and 33.7% for cyber-perpetration. Students consider some of the most common CB ways as posting victims’ embarrassing/humiliating videos on video-hosting sites; creating profiles on social media to humiliate victims and posting/forwarding victims’ private information/images without permission (NHS, 2015)
Concurrent La and A-site Vacancy Doping Modulates the Thermoelectric Response of SrTiO3. Experimental and Computational Evidence
Get PDFTo help understand the factors controlling the performance of one of the most promising n-type oxide thermoelectric SrTiO3, we need to explore structural control at the atomic level. In Sr1–xLa2x/3TiO3 ceramics (0.0 ≤ x ≤ 0.9), we determined that the thermal conductivity can be reduced and controlled through an interplay of La-substitution and A-site vacancies and the formation of a layered structure. The decrease in thermal conductivity with La and A-site vacancy substitution dominates the trend in the overall thermoelectric response. The maximum dimensionless figure of merit is 0.27 at 1070 K for composition x = 0.50 where half of the A-sites are occupied with La and vacancies. Atomic resolution Z-contrast imaging and atomic scale chemical analysis show that as the La content increases, A-site vacancies initially distribute randomly (x < 0.3), then cluster (x ≈ 0.5), and finally form layers (x = 0.9). The layering is accompanied by a structural phase transformation from cubic to orthorhombic and the formation of 90° rotational twins and antiphase boundaries, leading to the formation of localized supercells. The distribution of La and A-site vacancies contributes to a nonuniform distribution of atomic scale features. This combination induces temperature stable behavior in the material and reduces thermal conductivity, an important route to enhancement of the thermoelectric performance. A computational study confirmed that the thermal conductivity of SrTiO3 is lowered by the introduction of La and A-site vacancies as shown by the experiments. The modeling supports that a critical mass of A-site vacancies is needed to reduce thermal conductivity and that the arrangement of La, Sr, and A-site vacancies has a significant impact on thermal conductivity only at high La concentration
Studying design abduction in the context of novelty
Get PDFDesign abduction has been studied over the last several decades in order to increase our understanding in design reasoning. Yet, there is still considerable confusion and ambiguity regarding this topic. Some scholars contend that all regressive inferences in design — and design is mostly done by such backwards or regressive reasoning — are in fact abductions. Others focus on formal syllogistic forms in their attempt to clarify abduction. In contrast, we argue here that a defining characteristic of abduction is the production of, or the potential to produce, novel outcomes. Novelty is shown to be relative and depend mostly on what is known to the “reasoner” at the time of making the inference. Novelty is also shown to not necessarily be part of the direct outcome of an abductive inference; but rather, an attribute of an abductive design strategy that is intended to produce a new idea
Cross-ratio uninorms as an effective aggregation mechanism in sentiment analysis
Get PDFThere are situations in which lexicon-based methods for Sentiment Analysis (SA) are not able to generate a classification output for specific instances of a given dataset. Most often, the reason for this situation is the absence of specific terms in the sentiment lexicon required in the classification effort. In such cases, there were only two possible paths to follow: (1) add terms to the lexicon (off-line process) by human intervention to guarantee no noise is introduced into the lexicon, which prevents the classification system to provide an immediate answer; or (2) use the services of a word-frequency dictionary (on-line process), which is computationally costly to build. This paper investigates an alternative approach to compensate for the lack of ability of a lexicon-based method to produce a classification output. The method is based on the combination of the classification outputs of non lexicon-based tools. Specifically, firstly the outcome values of applying two or more non-lexicon classification methods are obtained. Secondly, these non-lexicon outcomes are fused using a uninorm based approach, which has been proved to have desirable compensation properties as required in the SA context, to generate the classification output the lexicon based approach is unable to achieve. Experimental results based on the execution of two well-known supervised machine learning algorithms, namely Naïve Bayes and Maximum Entropy, and the application of a cross-ratio uninorm operator are presented. Performance indices associated to options (1) and (2) above are compared against the results obtained using the proposed approach for two different datasets. Additionally, the performance of the proposed cross-ratio uninorm operator based approach is also compared when the aggregation operator used is the arithmetic mean instead. It is shown that the combination of non lexicon-based classification methods with specific uninorm operators improves the classification performance of lexicon-based methods, and it enables the offering of an alternative solution to the SA classification problem when needed. The proposed aggregation method could be used as well as a replacement of ensemble averaging techniques commonly applied when combining the results of several machine learning classifiers’ outputs
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