Evidence map›Paper›PMID 42220394›Full record

ArticleFrontiers in psychology2026

Personality traits and motivational patterns of social media use among undergraduate medical students at King Faisal University, Saudi Arabia.

Ayoob Lone, Faisal Waleed Aloraifi, Joud Mohammed Almulla, Hassan Ali Almirza, Saad Habib Alotaibi, Yousif Mohamed Ahmed Elmosaad, Nawaf Al Khashram, Sayed Abdul Qadar Quadri, Naveed Gani

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Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

Who cites it

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

9 authors.

Ayoob LoneDepartment of Clinical Neurosciences, College of Medicine, King Faisal University, Al-Ahsa, Saudi Arabia.
Faisal Waleed AloraifiCollege of Medicine, King Faisal University, Al-Ahsa, Saudi Arabia.
Joud Mohammed AlmullaCollege of Medicine, King Faisal University, Al-Ahsa, Saudi Arabia.
Hassan Ali AlmirzaCollege of Medicine, King Faisal University, Al-Ahsa, Saudi Arabia.
Saad Habib AlotaibiCollege of Medicine, King Faisal University, Al-Ahsa, Saudi Arabia.
Yousif Mohamed Ahmed ElmosaadDepartment of Public Health, College of Applied Medical Sciences, King Faisal University, Al-Ahsa, Saudi Arabia.
Nawaf Al KhashramDepartment of Biomedical Sciences, College of Medicine, King Faisal University, Al-Ahsa, Saudi Arabia.
Sayed Abdul Qadar QuadriDepartment of Biomedical Sciences, College of Medicine, King Faisal University, Al-Ahsa, Saudi Arabia.
Naveed GaniDepartment of Clinical Neurosciences, College of Medicine, King Faisal University, Al-Ahsa, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Social media has become deeply embedded in the daily lives of medical students, shaping communication, learning, and leisure activities. While demographic predictors of social media use are well documented, the role of personality traits in shaping distinct motivational patterns remains underexplored in the Saudi Arabian context. Aim and objectives: Drawing on the Big Five personality framework, this study examined how Big Five personality traits predict socialization, entertainment, informational, and academic motives for social media engagement among medical students. Methods: A cross-sectional survey was conducted among 370 undergraduate medical students at King Faisal University, Saudi Arabia. Participants completed the Big Five Inventory-10 and the Social Networking Usage Questionnaire. Hierarchical multiple regression analyses assessed the unique contribution of personality traits to social media motives after controlling for sociodemographic characteristics and patterns of social media engagement. Results: Extraversion and neuroticism were both positively associated with socialization ( Conclusion: Personality traits exert a differentiated and meaningful influence on the motivational drivers of social media use among medical students. Extraversion and neuroticism primarily shape socially and emotionally oriented engagement, openness facilitates exploratory information-seeking, and conscientiousness supports regulated entertainment use. These findings underscore the value of personality-informed approaches in understanding digital behavior within medical education and may inform targeted interventions to promote adaptive social media engagement.

Indexed as

medical studentsmotivational patternpersonality traitsSaudi Arabiasocial media

Identifiers

PMID42220394
PMCPMC13217398

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.