Rural Telecommunications Infrastructure Selection Using the Analytic Network Process.

The decisions involved in rural settings are of complex nature, with some aspects compounded by the presence of intangible criteria. Hence, a suitable approach is needed that can produce effective solutions. This paper describes the applicability of a multicriteria decision-making method, specifically the analytic network process (ANP), to model the selection of an appropriate telecommunications infrastructure technology, capable of deploying e-services in rural areas of developing countries. It aims to raise awareness among telecommunication planners about the availability of ANP, and to demonstrate its suitability to enhance the selection process. The proposed model is constructed based on concerned experts' views of relevant selection criteria and potential technology alternatives. Its network structure caters for all possible dependencies and interactions among criteria and alternatives

Rural Telecommunications Infrastructure Selection Using the Analytic Network Process, Journal of Telecommunications and Information Technology, 2010, nr 2

The decisions involved in rural settings are of complex nature, with some aspects compounded by the presence of intangible criteria. Hence, a suitable approach is needed that can produce effective solutions. This paper describes the applicability of a multicriteria decision-making method, specifically the analytic network process (ANP), to model the selection of an appropriate telecommunications infrastructure technology, capable of deploying e-services in rural areas of developing countries. It aims to raise awareness among telecommunication planners about the availability of ANP, and to demonstrate its suitability to enhance the selection process. The proposed model is constructed based on concerned experts’ views of relevant selection criteria and potential technology alternatives. Its network structure caters for all possible dependencies and interactions among criteria and alternatives

A decision support model for identification and prioritization of key performance indicators in the logistics industry

YesPerformance measurement of logistics companies is based upon various performance indicators. Yet, in the logistics industry, there are several vaguenesses, such as deciding on key indicators and determining interrelationships between performance indicators. In order to resolve these vaguenesses, this paper first presents the stakeholder-informed Balanced Scorecard (BSC) model, by incorporating financial (e.g. cost) and non-financial (e.g. social media) performance indicators, with a comprehensive approach as a response to the major shortcomings of the generic BSC regarding the negligence of different stakeholders. Subsequently, since the indicators are not independent of each other, a robust multi-criteria decision making technique, the Analytic Network Process (ANP) method is implemented to analyze the interrelationships. The integration of these two techniques provides a novel way to evaluate logistics performance indicators from logisticians' perspective. This is a matter that has not been addressed in the logistics industry to date, and as such remains a gap that needs to be investigated. Therefore, the proposed model identifies key performance indicators as well as various stakeholders in the logistics industry, and analyzes the interrelationships among the indicators by using the ANP. Consequently, the results show that educated employee (15.61%) is the most important indicator for the competitiveness of logistics companies

O36 The impact of the COVID-19 pandemic on undergraduate medical education: a survey of students’ safety and satisfaction during breast surgery clinical placement

Abstract Introduction COVID-19 is affecting all medical education and training. The University used in the collection of data suspended all clinical placement from mid-March 2020, resuming in-person teaching from September. To enable social distancing, the medical school and Breast Unit introduced: 1. one student per clinician per clinical activity (3-hours), 2. online learning (1-hr) and patient exposure (2 hours) in some clinical activities, 3. remote learning via Teams, and 4. personal protective equipment. Method We sent a 24-question survey to 31, 3rd and 4th year, students, who had breast surgery clinical placement between 07/09/20 and 18/12/20. The aim was to assess whether clinical activities could still feasibly be carried out, the effectiveness of COVID-19 protection, and students’ learning satisfaction. Result Our survey achieved a 65% response-rate. Over two-thirds of students had at least 3 days’ clinical placement, attending clinics, theatre, mammography, multidisciplinary team meetings and a 3-hr lecture via Teams. 90% of students had face-to-face patient interaction and 70% conducted physical examinations. All students were provided with hand-gel and masks and, at clinics, 35% of students were provided with face-shields. None of the students reported COVID-19 related symptoms during or after placement. 85% of students felt safe during their clinical placement and 95% reported satisfaction with the quality of teaching. Conclusion Notwithstanding COVID-19 restrictions, a blend of face-to-face with online clinical teaching can be safely delivered. Take-home Message Notwithstanding COVID-19 restrictions, a blend of face-to-face with online clinical teaching can be safely delivered

Tacrolimus (FK506) for induction of remission in corticosteroid-refractory ulcerative colitis

Background There are a limited number of treatment options for people with corticosteroid‐refractory ulcerative colitis. Animal models of inflammatory bowel disease and uncontrolled studies in humans suggest that tacrolimus may be an effective treatment for ulcerative colitis. Objectives To evaluate the efficacy and safety of tacrolimus for induction of remission in people with corticosteroid‐refractory ulcerative colitis. Search methods We searched the Cochrane Gut group specialised register, CENTRAL, MEDLINE (PubMed), Embase, Clinicaltrials.gov and WHO ICTRP from inception to October 2021 to identify relevant randomised controlled trials (RCT). Selection criteria Two review authors independently selected potentially relevant studies to determine eligibility based on the prespecified criteria. Data collection and analysis Two review authors independently extracted data and analysed them using Review Manager Web. The primary outcomes were induction of remission and clinical improvement, as defined by the studies and expressed as a percentage of the participants randomised (intention‐to‐treat analysis). Main results This review included five RCTs with 347 participants who had active ulcerative colitis or ulcerative proctitis. The duration of intervention varied between two weeks and eight week

A scoping review of artificial intelligence in medical education:BEME Guide No. 84

BACKGROUND: Artificial Intelligence (AI) is rapidly transforming healthcare, and there is a critical need for a nuanced understanding of how AI is reshaping teaching, learning, and educational practice in medical education. This review aimed to map the literature regarding AI applications in medical education, core areas of findings, potential candidates for formal systematic review and gaps for future research. METHODS: This rapid scoping review, conducted over 16?weeks, employed Arksey and O'Malley's framework and adhered to STORIES and BEME guidelines. A systematic and comprehensive search across PubMed/MEDLINE, EMBASE, and MedEdPublish was conducted without date or language restrictions. Publications included in the review spanned undergraduate, graduate, and continuing medical education, encompassing both original studies and perspective pieces. Data were charted by multiple author pairs and synthesized into various thematic maps and charts, ensuring a broad and detailed representation of the current landscape. RESULTS: The review synthesized 278 publications, with a majority (68%) from North American and European regions. The studies covered diverse AI applications in medical education, such as AI for admissions, teaching, assessment, and clinical reasoning. The review highlighted AI's varied roles, from augmenting traditional educational methods to introducing innovative practices, and underscores the urgent need for ethical guidelines in AI's application in medical education. CONCLUSION: The current literature has been charted. The findings underscore the need for ongoing research to explore uncharted areas and address potential risks associated with AI use in medical education. This work serves as a foundational resource for educators, policymakers, and researchers in navigating AI's evolving role in medical education. A framework to support future high utility reporting is proposed, the FACETS framework