This study aimed to evaluate the children's usage and their physical activity levels at playgrounds with (N = 4) and without (N = 4) organized sports activities, following a quasi-experimental study design. Direct observations were used to assess the playground usage and estimate the playground users' age category, sex, and physical activity intensity level. The results indicated that playgrounds with sports activities were associated with 53% more users at the time of the activities. However, this increase was only seen in boys. Furthermore, playgrounds with sport activities were not associated with different physical activity levels in children as compared to children on regular playgrounds.
Objectives: Aiming to reduce distributed denial-of-service (DDoS) attacks by alerting the consciences of Internet users, this paper evaluates the effectiveness of four warning banners displayed as online ads (deterrent—control, social, informative, and reorienting) and the contents of their two linked landing pages. Methods: We implement a 4 x 2 quasi-experimental design on a self-selected sample of Internet users to measure the engagement generated by the ads and the pages. Engagement is measured on the ads as the ratio of clicks to impressions, and on the pages as percentage of page scrolled, average session duration, video interaction rate, and URLs click rate. Results: Social ads generate significantly more engagement than the rest with low to medium effect sizes. Data reveal no differences in engagement between both landing page designs. Conclusions: Social messages may be a better alternative for engaging with potential cyber offenders than the traditional deterrent messages. Correspondence: Netherlands Institute for the Study of Crime and Law Enforcement (NSCR), De Boelelaan 1077, 1081 HV, Amsterdam, The Netherlands. Email: AMoneva@nscr.n This is a post-peer-review, pre-copyedit version of an article published in Journal of Experimental Criminology. The final authenticated version is available online at: https://link.springer.com/article/10.1007/s11292-022-09504-2
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Aims: To evaluate the effects of the implementation of a professional practice model based on Magnet principles on the nurse work environment in a Dutch teaching hospital. Design: A quasi-experimental study. Methods: Data were collected from registered nurses working on the clinical wards and outpatient clinics of the hospital in June/July 2016 (baseline) and in June/September 2019 (measurement of effects). Participants completed the Dutch Essentials of Magnetism II survey, which was used to measure their perception of their work environment. After baseline measurements were collected, interventions based on a professional practice model incorporating Magnet principles were implemented to improve the nurse work environment. Descriptive statistics and independent t-tests were conducted to examine differences between survey outcomes in 2016 and 2019. Results: Survey outcomes revealed significant changes in the nurse work environment between 2016 and 2019. Seven of the eight subscales (essentials of magnetism) improved significantly. Score for overall job satisfaction increased from 7.3 to 8.0 and score for quality of care increased from 7.0 to 7.6. On unit level, 17 of the 19 units showed improvement in the nurse work environment. Conclusion: The implementation of a professional practice model positively affects the nurse work environment, job satisfaction and quality of care. Impact: Nowadays, the quality of care is threatened by workload pressure and the low autonomy experienced by nurses. Considering the global shortage of nurses and growing complexity of healthcare, it is important to invest in improving the nurse work environment. The Magnet concept created a work environment in which nurses can deliver optimal quality of care. Knowledge of how Magnet principles affect the nurse work environment in the Netherlands is missing. These study results, including the description of how the interventions were implemented, will assist other hospitals to develop improvement strategies by focusing on the nurse work environment.
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Despite the benefits of the widespread deployment of diverse Internet-enabled devices such as IP cameras and smart home appliances - the so-called Internet of Things (IoT) has amplified the attack surface that is being leveraged by cyber criminals. While manufacturers and vendors keep deploying new products, infected devices can be counted in the millions and spreading at an alarming rate all over consumer and business networks. The objective of this project is twofold: (i) to explain the causes behind these infections and the inherent insecurity of the IoT paradigm by exploring innovative data analytics as applied to raw cyber security data; and (ii) to promote effective remediation mechanisms that mitigate the threat of the currently vulnerable and infected IoT devices. By performing large-scale passive and active measurements, this project will allow the characterization and attribution of compromise IoT devices. Understanding the type of devices that are getting compromised and the reasons behind the attacker’s intention is essential to design effective countermeasures. This project will build on the state of the art in information theoretic data mining (e.g., using the minimum description length and maximum entropy principles), statistical pattern mining, and interactive data exploration and analytics to create a casual model that allows explaining the attacker’s tactics and techniques. The project will research formal correlation methods rooted in stochastic data assemblies between IoT-relevant measurements and IoT malware binaries as captured by an IoT-specific honeypot to aid in the attribution and thus the remediation objective. Research outcomes of this project will benefit society in addressing important IoT security problems before manufacturers saturate the market with ostensibly useful and innovative gadgets that lack sufficient security features, thus being vulnerable to attacks and malware infestations, which can turn them into rogue agents. However, the insights gained will not be limited to the attacker behavior and attribution, but also to the remediation of the infected devices. Based on a casual model and output of the correlation analyses, this project will follow an innovative approach to understand the remediation impact of malware notifications by conducting a longitudinal quasi-experimental analysis. The quasi-experimental analyses will examine remediation rates of infected/vulnerable IoT devices in order to make better inferences about the impact of the characteristics of the notification and infected user’s reaction. The research will provide new perspectives, information, insights, and approaches to vulnerability and malware notifications that differ from the previous reliance on models calibrated with cross-sectional analysis. This project will enable more robust use of longitudinal estimates based on documented remediation change. Project results and methods will enhance the capacity of Internet intermediaries (e.g., ISPs and hosting providers) to better handle abuse/vulnerability reporting which in turn will serve as a preemptive countermeasure. The data and methods will allow to investigate the behavior of infected individuals and firms at a microscopic scale and reveal the causal relations among infections, human factor and remediation.
