DOEL. De laatste jaren zijn er nieuwe vormen van praktijkleren ontwikkeld binnen verpleegkundige opleidingen. Het doel van deze studie is onderzoeken of stage lopen binnen een krachtige leeromgeving leidt tot een sterkere ontwikkeling van ervaren self-efficacy bij hbo-verpleegkundestudenten dan stage lopen in een reguliere omgeving. METHODE. Een quasi experimenteel design (non-equivalent pretest-posttest control group) is toegepast, waarbij gebruik is gemaakt van de General Self Efficacy scale (GSE). De populatie bestaat uit hbo-v-stagiaires (n = 109 meting 1, n = 92 meting 2). Respondenten zijn onderverdeeld in studenten stage lopend binnen krachtige leeromgevingen en studenten stage lopend binnen reguliere stageomgevingen. Verschilscores op de GSE zijn voor beide groepen getoetst op significantie via t-toetsen. RESULTAAT. Studenten binnen krachtige leeromgevingen vertonen als totale groep en gedifferentieerd naar stage-ervaring op meer items van de GSE significante toename dan studenten binnen reguliere stageomgevingen. CONCLUSIE EN DISCUSSIE. Stage lopen binnen een krachtige leeromgeving lijkt in grotere mate bij te dragen aan de ontwikkeling van ervaren self-efficacy van hbo-verpleegkundestudenten. Het meten van self-efficacy binnen leeromgevingen is een aanvulling op bestaande uitkomstmaten voor het meten van effecten voor studenten. Verder onderzoek naar de relatie tussen de bronnen van self-efficacy en krachtige leeromgevingen binnen verpleegkundige opleidingen wordt aanbevolen.
The things teachers do in class have an important influence on their students’ motivation, engagement, and learning. This study uses an international expert panel to identify the teacher behaviors most likely to influence motivation—specifically, teacher behaviors that increase the more healthy, autonomous motivation that comes from within students. This list of behaviors, agreed upon by the experts, could be used by teachers trying to improve their practice, policymakers trying to scale interventions, and researchers trying to assess which behaviors best predict student outcomes.
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Background: COPD self-management is a complex behavior influenced by many factors. Despite scientific evidence that better disease outcomes can be achieved by enhancing self-management, many COPD patients do not respond to self-management interventions. To move toward more effective self-management interventions, knowledge of characteristics associated with activation for self-management is needed. The purpose of this study was to identify key patient and disease characteristics of activation for self-management. Methods: An explorative cross-sectional study was conducted in primary and secondary care in patients with COPD. Data were collected through questionnaires and chart reviews. The main outcome was activation for self-management, measured with the 13-item Patient Activation Measure (PAM). Independent variables were sociodemographic variables, self-reported health status, depression, anxiety, illness perception, social support, disease severity, and comorbidities. Results: A total of 290 participants (age: 67.2±10.3; forced expiratory volume in 1 second predicted: 63.6±19.2) were eligible for analysis. While poor activation for self-management (PAM-1) was observed in 23% of the participants, only 15% was activated for self-management (PAM-4). Multiple linear regression analysis revealed six explanatory determinants of activation for self-management (P,0.2): anxiety (β: -0.35; -0.6 to -0.1), illness perception (β: -0.2; -0.3 to -0.1), body mass index (BMI) (β: -0.4; -0.7 to -0.2), age (β: -0.1; -0.3 to -0.01), Global Initiative for Chronic Obstructive Lung Disease stage (2 vs 1 β: -3.2; -5.8 to -0.5; 3 vs 1 β: -3.4; -7.1 to 0.3), and comorbidities (β: 0.8; -0.2 to 1.8), explaining 17% of the variance. Conclusion: This study showed that only a minority of COPD patients is activated for self-management. Although only a limited part of the variance could be explained, anxiety, illness perception, BMI, age, disease severity, and comorbidities were identified as key determinants of activation for self-management. This knowledge enables health care professionals to identify patients at risk of inadequate self-management, which is essential to move toward targeting and tailoring of self-management interventions. Future studies are needed to understand the complex causal mechanisms toward change in self-management
The goal of UPIN is to develop and evaluate a scalable distributed system that enables users to cryptographically verify and easily control the paths through which their data travels through an inter-domain network like the Internet, both in terms of router-to-router hops as well as in terms of router attributes (e.g., their location, operator, security level, and manufacturer). UPIN will thus provide the solution to a very relevant and current problem, namely that it is becoming increasingly opaque for users on the Internet who processes their data (e.g., in terms of service providers their data passes through as well as what jurisdictions apply) and that they have no control over how it is being routed. This is a risk for people’s privacy (e.g., a malicious network compromising a user’s data) as well as for their safety (e.g., an untrusted network disrupting a remote surgery). Motivating examples in which (sensitive) user data typically travels across the Internet without user awareness or control are: - Internet of Things for consumers: sensors such as sleep trackers and light switches that collect information about a user’s physical environment and send it across the Internet to remote services for analysis. - Medical records: health care providers requiring medical information (e.g., health records of patients or remote surgery telemetry) to travel between medical institutions according to specified agreements. - Intelligent transport systems: communication plays a crucial role in future autonomous transportation systems, for instance to avoid freight drones colliding or to ensure smooth passing of trucks through busy urban areas. The UPIN project is novel in three ways: 1. UPIN gives users the ability to control and verify the path that their data takes through the network all the way to the destination endpoint, both in terms of hops and attributes of routers traversed. UPIN accomplishes this by adding and improving remote attestation techniques for on-path routers to existing path verification mechanisms, and by adopting and further developing in-packet path selection directives for control. 2. We develop and simulate data and control plane protocols and router extensions to include the UPIN system in inter-domain networking systems such as IP (e.g., using BGP and segment routing) and emerging systems such as SCION and RINA. 3. We evaluate the scalability and performance of the UPIN system using a multi-site testbed of open programmable P4 routers, which is necessary because UPIN requires novel packet processing functions in the data plane. We validate the system using the earlier motivating examples as use cases. The impact we target is: - Increased trust from users (individuals and organizations) in network services because they are able to verify how their data travels through the network to the destination endpoint and because the UPIN APIs enable novel applications that use these network functions. - More empowered users because they are able to control how their data travels through inter-domain networks, which increases self-determination, both at the level of individual users as well as at the societal level.
