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Record W3216812964 · doi:10.1080/13548506.2021.2005249

Anxiety and emotional-behavioral problems of adolescents in China: evidence for a serial mediation model of alexithymia and dependency

2021· article· en· W3216812964 on OpenAlexaboutno aff
Lijuan Liang, Wei Zhu, Juan Yang, Fei Wang

Bibliographic record

VenuePsychology Health & Medicine · 2021
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaPsychologyAnxietyMediationToronto Alexithymia ScaleClinical psychologyDependency (UML)Bivariate analysisPopulationDevelopmental psychologyPsychiatryMedicine

Abstract

fetched live from OpenAlex

High prevalence of emotional and behavioral problems among Chinese adolescence has been reported. This study seeks to investigate the mediating effect of alexithymia and dependency on anxiety and emotional-behavioral problems among adolescents. The study population included 519 adolescents. The assessments included the completion of standardized scales such as the Multidimensional Anxiety Scale (MASC), the Toronto Alexithymia Scale (TAS-20), the Depressive Experiences Questionnaire (DEQ), the Strengths and Difficulties Questionnaire (SDQ). Independent-sample t-tests, bivariate correlation, and serial mediation analyses were performed using SPSS23.0. Bivariate analyses revealed that anxiety, emotional-behavioral problem, alexithymia, and dependency were positively correlated. Alexithymia and dependency play a significant role in mediating the effect of multidimensional anxiety on emotional-behavioral problems. The effects of the two mediating paths were 69.86% and 7.81% for indirect effect through alexithymia, dependency, and specific indirect effect by alexithymia and dependency was 12.33%. Anxiety and emotional-behavioral problems mediate the relationship between alexithymia and dependency.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.078
GPT teacher head0.414
Teacher spread0.336 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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