Abstract Background: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes coronavirus disease 2019 (COVID-19), has challenged healthcare globally. An acute increase in the number of hospitalized patients has neces‑ sitated a rigorous reorganization of hospital care, thereby creating circumstances that previously have been identifed as facilitating prescribing errors (PEs), e.g. a demanding work environment, a high turnover of doctors, and prescrib‑ ing beyond expertise. Hospitalized COVID-19 patients may be at risk of PEs, potentially resulting in patient harm. We determined the prevalence, severity, and risk factors for PEs in post–COVID-19 patients, hospitalized during the frst wave of COVID-19 in the Netherlands, 3months after discharge. Methods: This prospective observational cohort study recruited patients who visited a post-COVID-19 outpatient clinic of an academic hospital in the Netherlands, 3months after COVID-19 hospitalization, between June 1 and October 1 2020. All patients with appointments were eligible for inclusion. The prevalence and severity of PEs were assessed in a multidisciplinary consensus meeting. Odds ratios (ORs) were calculated by univariate and multivariate analysis to identify independent risk factors for PEs. Results: Ninety-eight patients were included, of whom 92% had ≥1 PE and 8% experienced medication-related harm requiring an immediate change in medication therapy to prevent detoriation. Overall, 68% of all identifed PEs were made during or after the COVID-19 related hospitalization. Multivariate analyses identifed ICU admission (OR 6.08, 95% CI 2.16–17.09) and a medical history of COPD / asthma (OR 5.36, 95% CI 1.34–21.5) as independent risk fac‑ tors for PEs. Conclusions: PEs occurred frequently during the SARS-CoV-2 pandemic. Patients admitted to an ICU during COVID19 hospitalization or who had a medical history of COPD / asthma were at risk of PEs. These risk factors can be used to identify high-risk patients and to implement targeted interventions. Awareness of prescribing safely is crucial to prevent harm in this new patient population.
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Aim: In-hospital prescribing errors (PEs) may result in patient harm, prolonged hospitalization and hospital (re)admission. These events are associated with pressure on healthcare services and significant healthcare costs. To develop targeted interventions to prevent or reduce in-hospital PEs, identification and understanding of facilitating and protective factors influencing in-hospital PEs in current daily practice is necessary, adopting a Safety-II perspective. The aim of this systematic review was to create an overview of all factors reported in the literature, both protective and facilitating, as influencing in-hospital PEs. Methods: PubMed, EMBASE.com and the Cochrane Library (via Wiley) were searched, according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement, for studies that identified factors influencing in-hospital PEs. Both qualitative and quantitative study designs were included. Results: Overall, 19 articles (6 qualitative and 13 quantitative studies) were included and 40 unique factors influencing in-hospital PEs were identified. These factors were categorized into five domains according to the Eindhoven classification (‘organization-related’, ‘prescriber-related’, ‘prescription-related’, ‘technologyrelated’ and ‘unclassified’) and visualized in an Ishikawa (Fishbone) diagram. Most of the identified factors (87.5%; n = 40) facilitated in-hospital PEs. The most frequently identified facilitating factor (39.6%; n = 19) was ‘insufficient (drug) knowledge, prescribing skills and/or experience of prescribers’. Conclusion: The findings of this review could be used to identify points of engagement for future intervention studies and help hospitals determine how to optimize prescribing. A multifaceted intervention, targeting multiple factors might help to circumvent the complex challenge of in-hospital PEs.
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Kornell, Hays, and Bjork ([2009]. Unsuccessful retrieval attempts enhance subsequent learning. Journal of Experimental Psychology: Learning, Memory, and Cognition,35, 989–998) showed that incorrect guesses do not necessarily harm and might even improve the retention of information on a subsequent test. We sought to replicate the finding using educationally relevant stimuli. In two experiments, our participants either translated sentences in a foreign language receiving immediate feedback (errorful condition), or copied and studied the correct translation (errorless condition). After this training phase, a final test with the same sentences showed that translating sentences wrongly during training did not lower the accuracy of the errorful as compared to the errorless condition. Overall there was evidence that errorful training produced superior learning of the meaning and grammar of the foreign language sentences. The results support the idea that search processes activate a greater network of related knowledge in the errorful than in the errorless condition.
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This article reports on a literature review on empirical research investigating learning for vocations in the context of vocational education. We included 36 studies in which learning for vocations is empirically studied. Learning for vocations is characterised based upon prevalent research traditions in the field and framed from the perspective of vocational education and organised learning practices. This framing and characterisation directed the search terms for the review. Results show empirical data on vocational learning and illustrate how learning processes for the functions of vocational education - vocational identity development, development of a vocational repertoire of actions, and vocational knowledge development - actually take place. The review further shows that, empirical illustrations of learning processes that occur in the context of vocational education and organised learning practices are relatively scarce. The findings can be typified in relation to our theoretical framework in terms of three learning processes, that is learning as a process of (a) belonging, becoming, and being, (b) recontextualization, and (c) negotiation of meaning and sense-making. We argue that more empirical research should be carried out, using the functions of vocational education and the three learning processes to better understand vocational learning.
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Active learning has become an increasingly popular method for screening large amounts of data in systematic reviews and meta-analyses. The active learning process continually improves its predictions on the remaining unlabeled records, with the goal of identifying all relevant records as early as possible. However, determining the optimal point at which to stop the active learning process is a challenge. The cost of additional labeling of records by the reviewer must be balanced against the cost of erroneous exclusions. This paper introduces the SAFE procedure, a practical and conservative set of stopping heuristics that offers a clear guideline for determining when to end the active learning process in screening software like ASReview. The eclectic mix of stopping heuristics helps to minimize the risk of missing relevant papers in the screening process. The proposed stopping heuristic balances the costs of continued screening with the risk of missing relevant records, providing a practical solution for reviewers to make informed decisions on when to stop screening. Although active learning can significantly enhance the quality and efficiency of screening, this method may be more applicable to certain types of datasets and problems. Ultimately, the decision to stop the active learning process depends on careful consideration of the trade-off between the costs of additional record labeling against the potential errors of the current model for the specific dataset and context.
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Active learning has become an increasingly popular method for screening large amounts of data in systematic reviews and meta-analyses. The active learning process continually improves its predictions on the remaining unlabeled records, with the goal of identifying all relevant records as early as possible. However, determining the optimal point at which to stop the active learning process is a challenge. The cost of additional labeling of records by the reviewer must be balanced against the cost of erroneous exclusions. This paper introduces the SAFE procedure, a practical and conservative set of stopping heuristics that offers a clear guideline for determining when to end the active learning process in screening software like ASReview. The eclectic mix of stopping heuristics helps to minimize the risk of missing relevant papers in the screening process. The proposed stopping heuristic balances the costs of continued screening with the risk of missing relevant records, providing a practical solution for reviewers to make informed decisions on when to stop screening. Although active learning can significantly enhance the quality and efficiency of screening, this method may be more applicable to certain types of datasets and problems. Ultimately, the decision to stop the active learning process depends on careful consideration of the trade-off between the costs of additional record labeling against the potential errors of the current model for the specific dataset and context.
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Motor learning is particularly challenging in neurological rehabilitation: patients who suffer from neurological diseases experience both physical limitations and difficulties of cognition and communication that affect and/or complicate the motor learning process. Therapists (e.g.,, physiotherapists and occupational therapists) who work in neurorehabilitation are therefore continuously searching for the best way to facilitate patients during these intensive learning processes. To support therapists in the application of motor learning, a framework was developed, integrating knowledge from the literature and the opinions and experiences of international experts. This article presents the framework, illustrated by cases from daily practice. The framework may assist therapists working in neurorehabilitation in making choices, implementing motor learning in routine practice, and supporting communication of knowledge and experiences about motor learning with colleagues and students. The article discusses the framework and offers suggestions and conditions given for its use in daily practice.
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Evaluation of the effect of Problem Based Learning course
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A B S T R A C T Background: Approximately 4 years ago a new concept of learning in practice called the ‘Learning and Innovation Network (LIN)’ was introduced in The Netherlands. To develop a definition of the LIN, to identify working elements of the LIN in order to provide a preliminary framework for evaluation, a concept analysis was conducted. Method: For the concept analysis, we adopted the method of Walker and Avant. We searched for relevant publications in the EBSCO host portal, grey literature and snowball searches, as well as Google internet searches and dictionary consults. Results: Compared to other forms of workplace learning, the LIN is in the centre of the research, education and practice triangle. The most important attributes of the LIN are social learning, innovation, daily practice, reflection and co-production. Often described antecedents are societal developments, such as increasing complexity of work, and time and space to learn. Frequently identified consequences are an attractive workplace, advancements of expertise of care professionals, innovations that endorse daily practice, improvement of quality of care and the integration of education and practice. Conclusions: Based on the results of the concept analysis, we describe the LIN as ‘a group of care professionals, students and an education representatives who come together in clinical practice and are all part of a learning and innovation community in nursing. They work together on practice-based projects in which they combine best practices, research evidence and client perspectives in order to innovate and improve quality of care and in which an integration of education, research and practice takes place’. We transferred the outcomes of the concept analysis to an input-throughput-output model that can be used as a preliminary framework for future research.
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Despite the promises of learning analytics and the existence of several learning analytics implementation frameworks, the large-scale adoption of learning analytics within higher educational institutions remains low. Extant frameworks either focus on a specific element of learning analytics implementation, for example, policy or privacy, or lack operationalization of the organizational capabilities necessary for successful deployment. Therefore, this literature review addresses the research question “What capabilities for the successful adoption of learning analytics can be identified in existing literature on big data analytics, business analytics, and learning analytics?” Our research is grounded in resource-based view theory and we extend the scope beyond the field of learning analytics and include capability frameworks for the more mature research fields of big data analytics and business analytics. This paper’s contribution is twofold: 1) it provides a literature review on known capabilities for big data analytics, business analytics, and learning analytics and 2) it introduces a capability model to support the implementation and uptake of learning analytics. During our study, we identified and analyzed 15 key studies. By synthesizing the results, we found 34 organizational capabilities important to the adoption of analytical activities within an institution and provide 461 ways to operationalize these capabilities. Five categories of capabilities can be distinguished – Data, Management, People, Technology, and Privacy & Ethics. Capabilities presently absent from existing learning analytics frameworks concern sourcing and integration, market, knowledge, training, automation, and connectivity. Based on the results of the review, we present the Learning Analytics Capability Model: a model that provides senior management and policymakers with concrete operationalizations to build the necessary capabilities for successful learning analytics adoption.
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