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Exploring the Link between Early Memories of Warmth and Mobile Phone Addiction: Mediating Role of Alexithymia and Moderating Effects of Positive Coping Styles

2025· article· en· W4411497175 on OpenAlexaboutno aff
Lijuan Huang, Xianliang Zheng

Bibliographic record

VenueCurrent Psychiatry Research and Reviews · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaPsychologyToronto Alexithymia ScaleCoping (psychology)AddictionMobile phoneModerationClinical psychologySocial psychologyPsychiatry

Abstract

fetched live from OpenAlex

Background: In the information age, mobile phones have become an important tool in people’s lives. However, prolonged and frequent use of mobile phones can lead to mobile phone addiction and have a negative impact on people’s physical and mental development. Objective: The aim of this study was to explore the direct relationship between Early Memories of Warmth and Safeness (EMWS) and Mobile Phone Addiction (MPA) among university students, as well as the mediating role of alexithymia and the moderating role of positive coping styles. Methods: A sample of 422 Chinese university students (M = 20.00 years, SD = 1.40 years) anonymously responded to the EMWS Scale, Toronto Alexithymia Scale, MPA scale, and Positive Coping Style Scale. Results: EMWS were negatively associated with MPA. Alexithymia mediated the relationship between EMWS and MPA. Positive coping styles moderated the relationship between EMWS and MPA and that between EMWS and alexithymia. Specifically, With the increase in the level of positive coping styles, the negative effects of EMWS on MPA and alexithymia were gradually weakened. Conclusion: Results indicated that EMWS was intimately related to MPA and that EMWS affected MPA through alexithymia, with positive coping styles playing a moderating role. The results have implications for the prevention and intervention of MPA among college students.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.596
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.084
GPT teacher head0.405
Teacher spread0.321 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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