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Record W3016926168 · doi:10.1002/smi.2947

Mobile phone use in young adults who <scp>self‐identify</scp> as being “Very stressed out” or “Zen”: An exploratory study

2020· article· en· W3016926168 on OpenAlexafffund
Danie Majeur, Sarah Leclaire, Catherine Raymond, Pierre‐Majorique Léger, Robert‐Paul Juster, Sonia Lupien

Bibliographic record

VenueStress and Health · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsHEC MontréalUniversité de MontréalInstitut Universitaire en Santé Mentale de Québec
FundersFonds de Recherche du Québec - SantéCanadian Institutes of Health Research
KeywordsMobile phoneExploratory researchPsychologyPhoneStress (linguistics)Association (psychology)Developmental psychologyClinical psychologyComputer sciencePsychotherapist

Abstract

fetched live from OpenAlex

Previous studies have reported a positive association between mobile phone use and psychological stress. However, the reasons why stressed out individuals use their cell phones more are not clear. To gain further insight on this relationship, we performed secondary analyses on a database of 87 healthy young adults aged 18-35 years who self-categorized themselves as being "Very stressed out" (N = 46) or "Zen" (N = 41). All participants were assessed for psychological stress, duration and nature (hedonistic vs. utilitarian) of mobile phone use, involvement with the mobile phone and levels of nomophobia. Results controlled for the exploratory nature of this study showed that although "Very stressed out" and "Zen" individuals used their mobile phone for the same amount of time and were equally involved with it, "Very stressed out" individuals reported a greater use of their mobile phone for hedonistic purposes and were more nomophobic than "Zen" individuals. The results of this exploratory study suggest that highly stressed out individuals might use hedonistic functions of their mobile phone as a tool to deal with stress, thus explaining why they present greater levels of nomophobia.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score0.993

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 studies0.0010.000
Scholarly communication0.0000.001
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 head0.055
GPT teacher head0.367
Teacher spread0.313 · 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

Citations10
Published2020
Admission routes2
Has abstractyes

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