Hubungan Self Efficacy dengan Quarter Life Crisis pada Mahasiswa Tingkat Akhir di Universitas Negeri Padang
Bibliographic record
Abstract
With a high level of self-efficacy, students can face quarter-life crisis easily. People who have self-efficacy will focus and do their tasks without any problems in difficult situations. Students who have self-efficacy are more confident and able to reach their full potential. In addition, it helps students acquire the skills needed for future success. The purpose of this study was to empirically investigate the relationship between self-efficacy of final year students at Padang State University and quarter-life crisis. This study combines cluster sampling strategy with quantitative approach. A total of 292 final year students at Padang State University became the research sample. With an error rate of 5%, this sample was taken using Isaac and Michael table. The data analysis method used is Pearson Product Moment. The results showed that quarter-life crisis and self-efficacy of final year students at Padang State University were each in the moderate range with a proportion of self-efficacy of 39.7% and quarter-life crisis of 28.4%. The research hypothesis stating that there is a significant negative relationship between self-efficacy and quarter life crisis in final year students at Padang State University is accepted based on the hypothesis test which produces a value of r = -0.469 with a significance level of p = 0.000 (p < 0.05).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
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".