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Record W2920572740 · doi:10.3389/fpsyg.2019.00598

The Relationship Between Parental Attachment and Mobile Phone Dependence Among Chinese Rural Adolescents: The Role of Alexithymia and Mindfulness

2019· article· en· W2920572740 on OpenAlexaboutno aff
Xiaoqing Li, Chenrui Hao

Bibliographic record

VenueFrontiers in Psychology · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaPsychologyToronto Alexithymia ScaleMindfulnessModerationModerated mediationPopularityMediationMobile phoneDevelopmental psychologyClinical psychologySocial psychology

Abstract

fetched live from OpenAlex

Mobile phone has experienced a significant increase in popularity among adolescents in recent years. Findings indicate dependence on mobile phone is related to poor parent-child relationship. However, previous research on mobile phone dependence (MPD) is scant and mainly focus on adult samples. In this view, the present study investigated the association between parental attachment and MPD as well as its influence mechanism, in sample of adolescents in rural China. Data were collected from three middle schools in rural areas of Jiangxi and Hubei Province ( N = 693, 46.46% female, M age = 14.88, SD = 1.77). Participants completed the Inventory of Parent and Peer Attachment (IPPA), the twenty-item Toronto alexithymia scale (TAS-20), the Mindful Attention Awareness Scale (MAAS) and the Mobile Phone Addiction Index Scale (MPAI). Among the results, parental attachment negatively predicted MPD and alexithymia were exerting partial mediation effect between parental attachment and MPD. Further, mindfulness acted as moderator of the relationship between alexithymia and MPD: The negative impact of alexithymia on MPD was weakened under the condition of high level of mindfulness. Knowledge of this mechanism could be useful for understanding adolescents’ MPD in terms of the interaction of multiple factors.

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)0.001
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 score0.026
Threshold uncertainty score0.566

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.010
GPT teacher head0.318
Teacher spread0.308 · 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

Citations59
Published2019
Admission routes1
Has abstractyes

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