Pengaruh Dukungan Sosial Teman Sebaya Terhadap Quarter Life Crisis Pada Generasi-Z
Bibliographic record
Abstract
Penelitian ini bertujuan untuk mengetahui hubungan antara dukungan sosial teman sebaya dengan quarter life crisis pada Generasi-Z di Indonesia. Pendekatan yang digunakan adalah kuantitatif korelasional dengan 150 partisipan berusia 18–29 tahun yang dipilih melalui teknik incidental sampling. Instrumen yang digunakan meliputi skala dukungan sosial teman sebaya (Sabila, 2022) berdasarkan empat aspek Sarafino dan Smith (2011) serta The Developmental Crisis Questionnaire (DCQ-12) dari Robinson, Wright, dan Smith (2022). Analisis data dilakukan menggunakan uji korelasi Spearman karena data tidak berdistribusi normal. Hasil penelitian menunjukkan adanya hubungan positif yang signifikan antara dukungan sosial teman sebaya dan quarter life crisis (r = 0,302; p < 0,001). Temuan ini menunjukkan bahwa dukungan sosial bersifat ambivalen: dapat memperkuat resiliensi dan kesejahteraan psikologis, tetapi juga berpotensi meningkatkan tekanan melalui perbandingan sosial negatif (Sarafino & Smith, 2011; Festinger, 1954). Oleh karena itu, peningkatan kualitas dukungan sosial yang empatik dan adaptif menjadi kunci agar hubungan pertemanan benar-benar berfungsi sebagai faktor protektif dalam menghadapi quarter life crisis pada Generasi-Z
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 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".