There are numerous school dropout prevention programs. However, few of them have undergone a rigorous implementation evaluation to understand their effects. This research presents two studies that evaluated the intervention fidelity and differential effects of Check & Connect (C&C), a targeted school dropout prevention program aimed at promoting student engagement and achievement. A total of 145 elementary school students (Study 1) and 200 secondary school students (Study 2) from two French-Canadian school boards (regional districts grouping elementary and secondary schools) received the C&C intervention for two years. In both studies, a clinical monitoring form was used to compare the intervention fidelity of each program component and active ingredient with what was initially planned. The relation between intervention fidelity and the effects of C&C on student engagement and achievement was analyzed using multiple linear regressions. Overall, the results show that intervention fidelity varies across elementary and secondary schools from one component to another and from one site to another. Furthermore, the association between the fidelity of each component and positive outcomes varies, depending on the implementation site. This evaluation supports the relevance of every component of C&C to favor engagement and academic achievement among at-risk elementary and secondary school students, while suggesting that the importance of certain program components may vary, depending on contextual influences on implementation and outcomes.
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http://dx.doi.org/10.1016/j.evalprogplan.2018.02.004 | DOI Listing |
Phys Rev Lett
December 2024
Quantinuum, 303 S. Technology Court, Broomfield, Colorado 80021, USA.
Although quantum mechanics underpins the microscopic behavior of all materials, its effects are often obscured at the macroscopic level by thermal fluctuations. A notable exception is a zero-temperature phase transition, where scaling laws emerge entirely due to quantum correlations over a diverging length scale. The accurate description of such transitions is challenging for classical simulation methods of quantum systems, and is a natural application space for quantum simulation.
View Article and Find Full Text PDFRepositioning a patient from the prone to supine position can delay the initiation of cardiopulmonary resuscitation (CPR). Investigators used high-fidelity simulation to assess the time to initiate chest compressions and the time during which compressions did not occur for supine and prone CPR. Sixty participants completed a knowledge assessment before and after attending an education session and completing two simulations (ie, supine, prone).
View Article and Find Full Text PDFHolographic light potentials generated by phase-modulating liquid-crystal spatial light modulators (SLMs) are widely used in quantum technology applications. Accurate calibration of the wavefront and intensity profile of the laser beam at the SLM display is key to the high fidelity of holographic potentials. Here, we present a new calibration technique that is faster than previous methods while maintaining the same level of accuracy.
View Article and Find Full Text PDFBMC Public Health
January 2025
The Matilda Centre for Research in Mental Health and Substance Use, The University of Sydney, Level 6, Jane Foss Russell Building, Sydney, NSW, 2006, Australia.
Background: Preventure is a selective school-based personality-targeted program that has shown long-term benefits in preventing student alcohol use, internalising and externalising problems when delivered by psychologists. In this first Australian randomised controlled trial of school staff implementation of Preventure, we aimed to examine i) acceptability, feasibility, and fidelity and ii) effectiveness of Preventure on student alcohol use, internalising, and externalising symptoms.
Methods: A cluster-randomised controlled implementation trial was conducted in Sydney, Australia and was guided by the RE-AIM framework (Glasgow et al.
Sci Rep
January 2025
Division of Plastic, Craniofacial and Hand Surgery, Sidra Medicine, and Weill Cornell Medical College, C1-121, Al Gharrafa St, Ar Rayyan, Doha, Qatar.
Training a machine learning system to evaluate any type of facial deformity is impeded by the scarcity of large datasets of high-quality, ethics board-approved patient images. We have built a deep learning-based cleft lip generator called CleftGAN designed to produce an almost unlimited number of high-fidelity facsimiles of cleft lip facial images with wide variation. A transfer learning protocol testing different versions of StyleGAN as the base model was undertaken.
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