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Record W2888789887 · doi:10.1002/pra2.2017.14505401133

Mobile device use to access information on well‐being

2017· article· en· W2888789887 on OpenAlexafffund
Cynthia Kumah, Jamshid Beheshti, Joan C. Bartlett

Bibliographic record

VenueProceedings of the Association for Information Science and Technology · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsMcGill University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsWell-beingMobile deviceTask (project management)Mental healthTracking (education)Focus (optics)PsychologyComputer scienceSpiritualityFocus groupInternet privacyApplied psychologyMultimediaWorld Wide WebSociologyMedicineEngineeringPedagogy

Abstract

fetched live from OpenAlex

ABSTRACT The focus of this study is to identify and describe the use of mobile devices for supporting and maintaining well‐being among millennials. Data collection involved semi‐structured interviews of 18 undergraduate students (aged 18–24). They primarily defined their personal well‐being in terms of physical health, mental health and spirituality. They used their mobile devices extensively in support of well‐being, including searching for information (both text and multi‐media), establishing and maintaining social connections, tracking routines (e.g., nutrition, sleep) and setting and pursuing fitness goals. Our initial findings demonstrate how mobile devices are used in support of an everyday life task such as maintaining personal well‐being.

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)0.019
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.669
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.019
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.001
Scholarly communication0.0010.015
Open science0.0020.000
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 head0.020
GPT teacher head0.334
Teacher spread0.314 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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

Citations2
Published2017
Admission routes2
Has abstractyes

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