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Implementing work-related Mental-health guidelines in general PRacticE (IMPRovE): findings of a parallel cluster randomised controlled trial
CONCLUSIONS: GP adherence to the Guideline improved as a result of receiving the multicomponent intervention. →
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Do illness beliefs predict uptake of depression treatment after web-based depression screening? A secondary analysis of the DISCOVER RCT
CONCLUSIONS: Results suggest considerable effects of IB for depression treatment uptake in untreated populations. →
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Population-Based Digital Health Interventions to Deliver at-Home COVID-19 Testing: SCALE-UP II Randomized Clinical Trial
CONCLUSIONS: Simple bidirectional text messaging was more effective than an interactive web-based chatbot on the delivery of COVID-19 testing. Although messaging every 10 days was more effective than every 30 days, it also led to a larger opt-out rate. Digital health interventions based on automated bidirectional SMS text messaging are a simple, scalable, and low-cost… →
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Too Much Thinking Can Break LLMs: Inverse Scaling in Test-Time Compute
«`html Too Much Thinking Can Break LLMs: Inverse Scaling in Test-Time Compute Recent advances in large language models (LLMs) have encouraged the idea that allowing models to “think longer” during inference typically improves their accuracy and robustness. Techniques such as chain-of-thought prompting, step-by-step explanations, and increasing “test-time compute” have become standard in the field. However,… →
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A Coding Guide to Build a Scalable Multi-Agent System with Google ADK
«`html Understanding the Target Audience for A Coding Guide to Build a Scalable Multi-Agent System with Google ADK The target audience for this tutorial primarily includes software developers, data scientists, and business analysts looking to leverage AI technologies for building scalable systems. These professionals typically work in enterprise settings and are interested in optimizing workflows… →
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Apple Researchers Introduce FastVLM: Achieving State-of-the-Art Resolution-Latency-Accuracy Trade-off in Vision Language Models
Understanding the Target Audience for FastVLM The target audience for the introduction of FastVLM comprises primarily AI researchers, machine learning practitioners, and business leaders interested in the implementation and optimization of Vision Language Models (VLMs) in enterprise applications. This audience typically has a strong technical background and is engaged in fields such as AI development,… →
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Is Vibe Coding Safe for Startups? A Technical Risk Audit Based on Real-World Use Cases
«`html Is Vibe Coding Safe for Startups? A Technical Risk Audit Based on Real-World Use Cases Introduction: Why Startups Are Looking at Vibe Coding Startups are under pressure to build, iterate, and deploy faster than ever. With limited engineering resources, many are exploring AI-driven development environments—collectively referred to as “Vibe Coding”—as a shortcut to launch… →
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MiroMind-M1: Advancing Open-Source Mathematical Reasoning via Context-Aware Multi-Stage Reinforcement Learning
«`html Understanding the Target Audience for MiroMind-M1 The MiroMind-M1 initiative targets a range of professionals involved in mathematics, AI, and machine learning. This includes researchers, data scientists, and AI developers who are seeking robust and transparent tools for mathematical reasoning. Their pain points often include a lack of transparency and reproducibility in proprietary models, as… →
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Rubrics as Rewards (RaR): A Reinforcement Learning Framework for Training Language Models with Structured, Multi-Criteria Evaluation Signals
«`html Rubrics as Rewards (RaR): A Reinforcement Learning Framework for Training Language Models with Structured, Multi-Criteria Evaluation Signals Researchers have proposed the Rubrics as Rewards (RaR) framework, which utilizes checklist-style rubrics to enhance reinforcement learning in training language models (LLMs). This method focuses on guiding multi-criteria tasks, producing prompt-specific rubrics based on structured principles. Each… →