We investigated the relationship between process quality in early childhood education and care (ECEC) and children’s socio-emotional development in a meta-analysis of longitudinal studies. Our multi-level meta-analysis of 31 publications reporting on 16 longitudinal studies (N = 17,913 children, age: 2.5–18 yrs) demonstrates that the process quality of ECEC is a small but significant predictor of children’s socio-emotional development over time (ES = 0.103, SE = 0.026, p < 0.001, 95% CI: 0.052–0.155). This longitudinal association extends to the age of 18 years in our sample. Process quality of ECEC is, thus, a significant and stable predictor of children’s socio-emotional development and well-being from toddlerhood to adolescence. The longitudinal relationship was moderated by the type of care (center-based vs. home-based) and the informant (parent, professional caregiver, external assessor, or self-report of the child). Implications for future ECEC research are discussed.
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OBJECTIVE: This work aims to gain insight into the long-term impact of depression course on social network size and perceived loneliness in older people living in the community. METHODS: Within a large representative sample of older people in the community (Longitudinal Aging Study Amsterdam (LASA)), participants with clinically relevant levels of depressive symptoms (scores >16 on the Center for Epidemiological Studies Depression Scale) were followed up over a period of 13 years of the LASA study (five waves). General estimating equations were used to estimate the impact of depression course on network size and loneliness and the interaction with gender and age. RESULTS: An unfavorable course of depression was found to be associated with smaller network sizes and higher levels of loneliness over time, especially in men and older participants. CONCLUSIONS: The findings of this study stress the importance of clinical attention to the negative consequences of chronicity in depressed older people. Clinicians should assess possible erosion of the social network over time and be aware of increased feelings of loneliness in this patient group.
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This three-wave study examined associations between a motivational construct future time perspective (FTP) and teachers’ identity (TI) in a sample of Dutch student teachers (N = 368). Additionally, gender and educational level were included as factors affecting FTP and TI. Random intercept cross-lagged panel models indicated that FTP and TI were moderately and positively correlated at the between-person level. At the within-person level, positive cross-lagged effects from wave 2 FTP on wave 3 TI were uncovered. Male student teachers and those that are academically educated reported weaker FTP and TI relations compared to their counterparts. Implications for practice are discussed.
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Physical rehabilitation programs revolve around the repetitive execution of exercises since it has been proven to lead to better rehabilitation results. Although beginning the motor (re)learning process early is paramount to obtain good recovery outcomes, patients do not normally see/experience any short-term improvement, which has a toll on their motivation. Therefore, patients find it difficult to stay engaged in seemingly mundane exercises, not only in terms of adhering to the rehabilitation program, but also in terms of proper execution of the movements. One way in which this motivation problem has been tackled is to employ games in the rehabilitation process. These games are designed to reward patients for performing the exercises correctly or regularly. The rewards can take many forms, for instance providing an experience that is engaging (fun), one that is aesthetically pleasing (appealing visual and aural feedback), or one that employs gamification elements such as points, badges, or achievements. However, even though some of these serious game systems are designed together with physiotherapists and with the patients’ needs in mind, many of them end up not being used consistently during physical rehabilitation past the first few sessions (i.e. novelty effect). Thus, in this project, we aim to 1) Identify, by means of literature reviews, focus groups, and interviews with the involved stakeholders, why this is happening, 2) Develop a set of guidelines for the successful deployment of serious games for rehabilitation, and 3) Develop an initial implementation process and ideas for potential serious games. In a follow-up application, we intend to build on this knowledge and apply it in the design of a (set of) serious game for rehabilitation to be deployed at one of the partners centers and conduct a longitudinal evaluation to measure the success of the application of the deployment guidelines.
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.
