This paper aims to offer a critical reflection on the way Talent Management (TM) is investigated in practice, by addressing the key issues regarding the quality (in terms of rigor and relevance) of academic empirical TM research and therefore the critical scrutiny of TM scholars’ work. We will argue that despite the growth in the quantity, the quality of many empirical TM papers is lagging behind and hindering the progress of the academic field of TM.
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Redesigning IT systems for specific user groups encompasses a lot of effort with respect to analysing and understanding user behaviour. The goal of this paper is to provide insights into patterns of behaviour of agricultural users, during the usage of a decision support system called OPTIRas (TM). This system aids agricultural users in their cultivar selection activities. We analyse logs resulting from OPTIRas (TM), and we get insights into user's navigational patterns. We claim that the results of our analysis can be used to support the redesign of decision support systems in order to address specific agricultural users' characteristics.
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This method paper presents a template solution for text mining of scientific literature using the R tm package. Literature to be analyzed can be collected manually or automatically using the code provided with this paper. Once the literature is collected, the three steps for conducting text mining can be performed as outlined below:• loading and cleaning of text from articles,• processing, statistical analysis, and clustering, and• presentation of results using generalized and tailor-made visualizations.The text mining steps can be applied to a single, multiple, or time series groups of documents.References are provided to three published peer reviewed articles that use the presented text mining methodology. The main advantages of our method are: (1) Its suitability for both research and educational purposes, (2) Compliance with the Findable Accessible Interoperable and Reproducible (FAIR) principles, and (3) code and example data are made available on GitHub under the open-source Apache V2 license.
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Background:Telemonitoring (TM), as part of telehealth, allows physiotherapists to monitor and coach their patients using remotely collected data. The use of TM requires a different approach compared with face-to-face treatment. Although a telehealth capability framework exists for health care professionals, it remains unclear what specific capabilities are required to use TM during physiotherapy treatments.Objective:This study aims to identify the capabilities required to use TM in physiotherapy treatment.Methods:An exploratory qualitative study was conducted following a constructivist semistructured grounded theory approach. Three heterogeneous focus groups were conducted with 15 lecturers of the School of Physiotherapy (Bachelor of Science Physiotherapy program) from the Amsterdam University of Applied Sciences. Focus group discussions were audiotaped and transcribed verbatim. Capabilities for using TM in physiotherapy treatment were identified during an iterative process of data collection and analysis, based on an existing framework with 4 different domains. Team discussions supported further conceptualization of the findings.Results:Sixteen capabilities for the use of TM in physiotherapy treatment were found addressing 3 different domains. Four capabilities were identified in the “digital health technologies, systems, and policies” domain, 7 capabilities in the “clinical practice and application” domain, and 5 capabilities in the “data analysis and knowledge creation” domain. No capabilities were identified in the “system and technology implementation” domain.Conclusions:The use of TM in physiotherapy treatment requires specific skills from physiotherapists. To best use TM in physiotherapy treatment, it is important to integrate these capabilities into the education of current and future physiotherapists.
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From the website of the publisher: "Botanicals, which are basis of many plant food supplements (PFS), constitute the major part of worldwide and historically founded traditional medicines (TM) used to cure diseases and maintain health. A recently published trilogy called “The Art and Science of Traditional Medicine”, published by Science/AAAS (2015a, b, 2014) reveals many research aspects and examples of TM including the complexity of efficacy and safety testing of botanicals. It also exemplifies currently available innovative options to ensure and improve quality of TM (from Traditional Chinese Medicine, to Ayurveda and classic Greek, Roman and Celts’ medicines). The WHO, who in view of their Traditional Medicine Strategy 2014–2023 triggered this trilogy, aims to promote TM as a worldwide affordable health care option." Co-authors Mario Dell’AgliEmail authorChiara Di LorenzoEnrico SangiovanniGary WilliamsonPaolo MeoniPatrizia Restani
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Objective: To annotate a corpus of randomized controlled trial (RCT) publications with the checklist items of CONSORT reporting guidelines and using the corpus to develop text mining methods for RCT appraisal. Methods: We annotated a corpus of 50 RCT articles at the sentence level using 37 fine-grained CONSORT checklist items. A subset (31 articles) was double-annotated and adjudicated, while 19 were annotated by a single annotator and reconciled by another. We calculated inter-annotator agreement at the article and section level using MASI (Measuring Agreement on Set-Valued Items) and at the CONSORT item level using Krippendorff's α. We experimented with two rule-based methods (phrase-based and section header-based) and two supervised learning approaches (support vector machine and BioBERT-based neural network classifiers), for recognizing 17 methodology-related items in the RCT Methods sections. Results: We created CONSORT-TM consisting of 10,709 sentences, 4,845 (45%) of which were annotated with 5,246 labels. A median of 28 CONSORT items (out of possible 37) were annotated per article. Agreement was moderate at the article and section levels (average MASI: 0.60 and 0.64, respectively). Agreement varied considerably among individual checklist items (Krippendorff's α= 0.06–0.96). The model based on BioBERT performed best overall for recognizing methodology-related items (micro-precision: 0.82, micro-recall: 0.63, micro-F1: 0.71). Combining models using majority vote and label aggregation further improved precision and recall, respectively. Conclusion: Our annotated corpus, CONSORT-TM, contains more fine-grained information than earlier RCT corpora. Low frequency of some CONSORT items made it difficult to train effective text mining models to recognize them. For the items commonly reported, CONSORT-TM can serve as a testbed for text mining methods that assess RCT transparency, rigor, and reliability, and support methods for peer review and authoring assistance. Minor modifications to the annotation scheme and a larger corpus could facilitate improved text mining models. CONSORT-TM is publicly available at https://github.com/kilicogluh/CONSORT-TM.
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Een interview met Arno van Roosmalen, voormalig stadsconservator in Rotterdam. Ter gelegenheid van tentoonstelling en publicatie 'De stad, de kunstenaars en het museum; 25 jaar Stadscollectie Rotterdam' in museum Boijmans Van Beuningen 8-6 tm 1-9-2013.
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Binnen de Bslim scholen in Groningen is er de afgelopen jaren een beweegmonitor afgenomen in inzicht te krijgen in het beweeggedrag van kinderen (groep 5 tm 8). In deze rapportage wordt een trendanalyse getoond op basis van de verzamelde data tussen 2016 en 2022 m.b.t. de volgende variabelen: Schermtijd, buiten spelen, lid van een sportvereneging en hoe leuk men de gymles ervaart.
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Geert-Jan van Bussel, bijzonder lector Digital Archiving & Compliance, sprak op dinsdag 16 oktober 2012 zijn lectorale rede uit in het Kohnstammzaal. Van Bussel sprak over de betrouwbaarheid van informatie en de manieren waarop ‘Digital Archiving’ en ‘Compliance’ de informatiestromen in organisaties besturen.
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