MétaCan
Menu
Back to cohort

The relationship between social media addiction levels and alexithymia in young people at home during pandemic process

2022· article· en· W4320062904 on OpenAlexaboutno aff
Melike Yavaş Çeli̇k, Fatma Karasu

Bibliographic record

VenueActa Scientiarum Health Sciences · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaToronto Alexithymia ScaleAddictionPsychologyScale (ratio)Affect (linguistics)Clinical psychologyPsychiatryGeographyCartography

Abstract

fetched live from OpenAlex

This study was conducted to determine the relationship between social media addiction levels and alexithymia in young people who were at home during the pandemic process. The descriptive and cross-sectional study was conducted with 520 young people between 01.01.2021-15.01.2021. Data were collected using a personal information form, Social Media Addiction Scale and Toronto Alexithymia Scale. Kruskal-Wallis, Mann-Whitney U tests and correlation and regression analysis were used to evaluate the data. The total score average of the Social Media Addiction Scale of the youth was 94.65 ± 37.63 and the total score average of the Toronto Alexithymia Scale was 50.04 ± 12.14. It was determined that 44.6% of the Toronto Alexithymia Scale received 51 points. A positive and moderate correlation was found between Social Media Addiction Scale and Toronto Alexithymia Scale (r = 0.463, p = 0.001). Social media addiction was found to affect alexithymia by 21.3% according to the regression analysis. It has been determined that the social media addiction levels of the young people are medium and their alexithymia levels are high. It has been found that there is a significant relationship between social media addiction and alexithymia.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0240.002
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.062
GPT teacher head0.364
Teacher spread0.302 · 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.

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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueActa Scientiarum Health SciencesSame topicImpact of Technology on AdolescentsFrench-language works237,207