Quarter Life Crisis Among Fresh Graduates: Causes, Impacts, and Coping Strategies
Bibliographic record
Abstract
The prevalence of the phenomenon of quarter life crisis (QLC) among fresh graduates has become increasingly evident in the context of contemporary social and economic challenges. QLC is characterized by a state of confusion, anxiety, and uncertainty regarding the transition to adulthood. This psychological distress is further exacerbated by the prevailing economic instability and high unemployment rates in Indonesia. The objective of this study is to identify the causes, impacts, and coping strategies of QLC among fresh graduates. The study will be guided by a Systematic Literature Review (SLR) method that adheres to the PRISMA guidelines. The findings indicated that social support, social media use, and economic conditions exerted a substantial influence on the QLC experience. The emotional distress experienced by the subjects was mitigated, to some extent, by the support provided by family and friends. Conversely, excessive social media use had a deleterious effect on the subjects' stress levels and self-confidence issues. The repercussions of the QLC encompassed elevated levels of emotional distress, familial discord, and uncertainty in discerning a vocational trajectory. This study underscores the necessity for suitable interventions to assist young adults in coping with the challenges posed by QLC and cultivating effective adaptation strategies.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".