Coping with quarter-life crisis: An analysis of the role of social support and coping stress on senior university students
Bibliographic record
Abstract
A quarter-life crisis is a phase in which individuals face a crisis in various aspects of life, such as prospects, romance, achievements, life goals, and careers. With appropriate support and the ability to manage stress, individuals can better confront challenges and succeed. This research aims to determine the influence of social support and stress coping on a quarter-life crisis. The study employs a quantitative approach with multiple linear regression analysis. The sample for this research consisted of 247 students, with 89 male and 158 female respondents aged between 21 and 25 years. The results of this research, based on data analysis at a significance level, indicate that for social support, the F-value is 36.598 with a p-value <.01, and social support has a 13% influence on the quarter-life crisis. For stress coping, the significance level yields an F-value of 93.425 with a p-value <.01, contributing to a 27.6% influence on the quarter-life crisis. Additionally, there is a significant influence between social support and stress coping on the quarter-life crisis, with an F-value of 54.992, p <.01 and R-squared .311, meaning that social support and stress coping together can contribute to a 31.1% influence on the quarter-life crisis. In conclusion, the presence or absence of social support and stress coping can affect the intensity of a quarter-life crisis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".