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Record W4324357342 · doi:10.47611/jsrhs.v11i3.3637

“A Cross-Comparative Study of Adolescent Mental Health and Social Media Use in South Korea and Canada”

2022· article· en· W4324357342 on OpenAlexaboutno aff
Ji-Yeon Seo, Leni Kim

Bibliographic record

VenueJournal of Student Research · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsMental healthHarmSocial mediaAutonomyPsychologyConsumption (sociology)Social psychologyDevelopmental psychologyPolitical scienceSociologyPsychiatry

Abstract

fetched live from OpenAlex

In an increasingly digital world, the mental health of adolescents is reflected in their interactions on social networking sites (SNS). As adolescence represents a pivotal transitional period characterized by an increase in social relationships, the impact of the COVID-19 pandemic and its transferal of in-person interactions to online spheres is a crucial component of adolescent mental health in 2022. Where excessive social media using during the lockdowns led to an increase in cyberbullying (highlighted in Canada’s updated criminal code) and eating disorders (explored through mukbang, or ‘escapist eating’ in South Korea), the type of SNS interactions adolescents engage in—comparative social media use and more passive, autonomous enjoyment—are critical to distinguishing between the positive and negative effects of social media on adolescent health. To identify the link between pre, during, and post-pandemic adolescent mental health, this study utilizes a comparative analysis of surveillance and harm within social media consumption in South Korea and Canada. For South Korean and Canadian adolescents, the distinction between active and passive social media use and the degree of online autonomy can define the level of harm to mental stability, emphasizing the importance of autonomous social media consumption in ‘safe digital spaces.’

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)0.004
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 score0.482
Threshold uncertainty score0.877

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 science0.0000.001
Research integrity0.0000.001
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 head0.215
GPT teacher head0.505
Teacher spread0.290 · 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
Published2022
Admission routes1
Has abstractyes

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