Analisis Faktor Internal Kejadian Quarter-Life Crisis Pada Mahasiswa Tingkat Akhir Angkatan 2017
Bibliographic record
Abstract
Pendahuluan: Quarter life-crisis adalah sebuah krisis psikologis yang terjadi pada masa transisi remaja menuju dewasa dan dunia perkuliahan menuju dunia nyata yang diakibatkan ketidakpastian masa depan. Periode ini akan terasa sangat sulit bagi individu dikarenakan banyak perubahan-perubahan, tantangan, dan permasalahan yang harus dihadapi. Tujuan dari penelitian ini adalah untuk menganalisis faktor internal kejadian quarter-life crisis pada mahasiswa tingkat akhir angkatan 2017 di Fakultas Keperawatan Universitas Airlangga. Metode: Desain penelitian yang digunakan adalah kuantitatif dengan pendekatan cross-sectional. Pemilihan subjek penelitian menggunakan teknik simple random sampling, dengan jumlah partisipan sebanyak 99 mahasiswa. Instrumen penelitian menggunakan kuesioner General Self-efficacy Scale, dan kuesioner Quarter Life-Crisis Diagnosis Quiz yang telah dimodifikasi. Analisis data yang digunakan adalah uji regresi logistik biner. Hasil: Usia berhubungan signifikan dengan quarter-life crisis (p=0,045), Jenis kelamin tidak berhubungan signifikan dengan quarter-life crisis (p=0,051). Self-efficacy tidak berhubungan dengan quarter-life crisis (p=0,057). Diskusi: Hal ini mengindikasikan perlunya penelitian lebih lanjut tentang analisis faktor internal kejadian quarter-life crisis pada mahasiswa mahasiswa tingkat akhir.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".