Ningzhe Zhu

Development and Validation of a Multilevel Deep-Learning Framework for Individualized Prediction in Clustered Data

Larissa Sust, Ramona Schoedel, Yannik Terhorst, Ningzhe Zhu et autres

Clustered data are common in psychological studies that collect repeated observations from individuals, classrooms, clinics, or other naturally occurring units. Such data offer rich opportunities for predicting psychological outcomes, but they also pose substantial analytic challenges. Predictors are usually high-dimensional, and the …

Development and Validation of a Multilevel Deep-Learning Framework for Individualized Prediction in Clustered Data

Ningzhe Zhu, Ramona Schoedel, Larissa N. N. Sust, Markus Bühner et autres

Clustered data are common in psychological studies that collect repeated observations from individuals, classrooms, clinics, or other naturally occurring units. Such data offer rich opportunities for predicting psychological outcomes, but they also pose substantial analytic challenges. Predictors are usually high-dimensional, and the …

Dataset for "Development and Validation of a Multilevel Deep-Learning Framework for Individualized Prediction in Clustered Data"

Ningzhe Zhu, Ramona Schoedel, Larissa Sust, Markus Bühner et autres

We present the dataset accompanying the article "Development and Validation of a Multilevel Deep-Learning Framework for Individualized Prediction in Clustered Data". The data were collected as part of the Smartphone Sensing Panel Study and include 508 participants, 6,771 participant-days, 37 person-level features, …

Development and Validation of a Multilevel Deep-Learning Framework for Individualized Prediction in Clustered Data

Markus Bühner, Yannik Terhorst, Ramona Schoedel, Larissa Sust et autres

Digital Phenotyping of Lifestyle Profiles and Mental Well-Being in German Adults: Prospective Longitudinal Cohort Study

Ningzhe Zhu, Ramona Schoedel, Larissa Sust, Markus Bühner et autres

Abstract Background Digital phenotyping uses passively collected smartphone-sensing data to characterize everyday behavior in naturalistic settings, and has become an important approach for studying mental well-being. Most previous studies have examined associations between individual sensing variables and mental health. However, mental well-being …

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Childhood Maltreatment and Emotional Intelligence Across Development: A Multilevel Meta-Analytic Review

Ao Ren, Kaixin Zhong, Y W Liu, Qian Liu et autres

In recent years, research on the impact of childhood maltreatment on emotional intelligence has expanded. However, inconsistencies persist due to variations in maltreatment types, participant age, sample characteristics, cultural context, and measurement approaches. Systematic meta-analytic integration of these findings remains lacking. The …

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Can Digital Phenotyping Reveal Lifestyles That Reflect Mental Well-Being? Results From a Population-Matched Sample (Preprint)

Ningzhe Zhu, Ramona Schoedel, Larissa Sust, Markus Bühner et autres

BACKGROUND Digital phenotyping uses passively collected digital-sensing data to characterize real-world behavioral patterns. Such data may help identify everyday lifestyles that are relevant to mental well-being, but most prior approaches have used variable-centered methods that focus on single behaviors rather than person-centered …

Trait or Style? Testing Whether Personality Fine-Tuning Produces Cross-Situational Consistency in Large Language Models

Hongqiao Shi, Ao Ren, Ningzhe Zhu, Jiaxu Zhao

Large language models (LLMs) can be prompted to adopt personas and interaction styles, but it remains unclear whether targeted fine-tuning on personality-labeled text induces only surface-level stylistic mimicry or more stable, trait-like behavioral regularities. Prior work on LLM personas has often focused …

Can Digital Phenotyping Reveal Lifestyles That Reflect Mental Well-Being? Results From a Population-Matched Sample.

Ningzhe Zhu, Ramona Schoedel, Larissa N. N. Sust, Markus Buehner et autres

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Can Digital phenotyping reveal lifestyles that reflect mental well-being? Results from a population-matched sample.

Ramona Schoedel, Yannik Terhorst, Larissa N. N. Sust, Markus Bühner et autres

Dataset for "Can Digital Phenotyping Reveal Lifestyles That Reflect Mental Well-Being? Results From A Quota-Matched Sample"

Ningzhe Zhu, Ramona Schoedel, Larissa N. N. Sust, Markus Bühner et autres

We present the dataset for the article "Can Digital Phenotyping Reveal Lifestyles That Reflect Mental Well-Being? Results From A Quota-Matched Sample." The data were collected as part of the Smartphone Sensing Panel Study and include 7,635 observations from N = 553 participants …

Trait or Style? Testing Whether Personality Fine-Tuning Produces Cross-Situational Consistency in Large Language Models

Ningzhe Zhu, Hongqiao Shi, Jiaxu Zhao, Ao Ren

Large language models (LLMs) can be prompted to adopt personas and interaction styles, but it remains unclear whether targeted fine-tuning on personality-labeled text induces only surface-level stylistic mimicry or more stable, trait-like behavioral regularities. Prior work on LLM personas has often focused …