PENGARUH ALEXITHYMIA DAN DUKUNGAN SOSIAL TERHADAP NOMOPHOBIA PADA EMERGING ADULTHOOD
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui pengaruh alexithymia dan dukungan sosial terhadap nomophobia pada emerging adulthood. Penelitian ini menggunakan pendekatan kuantitatif. Sampel penelitian terdiri dari 373 emerging adulthood.. Instrumen yang digunakan dalam penelitian ini yaitu Nomophobia Questionnaire (NMP-Q) milik Yildirim & Correia (2013) untuk mengukur nomophobia, Toronto Alexithymia Scale-20 (TAS-20) milik Bagby, dkk (1994) untuk mengukur alexithymia, dan Skala Dukungan sosial milik Reka Rahmanda (2019) untuk mengukur dukungan sosial. Teknik analisis data yang digunakan adalah regresi linear sederhana dan regresi linear berganda. Hasil penelitian ini menunjukkan bahwa (1) terdapat pengaruh positif alexithymia terhadap nomophobia, (2) tidak terdapat pengaruh dukungan sosial terhadap nomophobia. This study aims to determine the effect of alexithymia and social support on nomophobia in emerging adulthood. This study uses a quantitative approach. The research sample consisted of 373 emerging adulthood. The instruments used in this study are Yildirim & Correia's Nomophobia Questionnaire (NMP-Q) (2013) to measure nomophobia, Bagby, et al's Toronto Alexithymia Scale-20 (TAS-20) (1994) to measure alexithymia, and Reka Rahmanda's Social Support Scale (2019) to measure social support. The data analysis techniques used were simple linear regression and multiple linear regression. The results of this study indicate that (1) there is a positive influence of alexithymia on nomophobia, (2) there is no influence of social support on nomophobia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".