Psychological Distress in the Quarter-life Crisis: The Role of Psychological Inflexibility
Bibliographic record
Abstract
The developmental transition from adolescence to adulthood involves many developmental demands, so many individuals experience the Quarter-life crisis phenomenon. Individuals experiencing this crisis may experience increased psychological distress. Psychological distress can disrupt life, so it needs to be treated seriously. This research aims to determine the relationship between psychological inflexibility and psychological distress in individuals experiencing a quarter-life crisis. This research uses a quantitative approach with a correlational design. Participants consisted of 107 individuals aged 20-29 who were experiencing a quarter-life crisis and were selected using purposive sampling. Research measurements were conducted using The Hopkins Symptoms Checklist-25 (α = 0.894) and Acceptance and Action Questionnaire-II (α = 0.822). Data analysis was carried out using simple regression test statistical techniques. The results of the analysis show that high psychological inflexibility can predict higher psychological distress (R2 = 0.249; p < 0.001). Psychological inflexibility contributed 24.9% to psychological distress. It can be concluded that individuals in QLC who have rigid psychological reaction patterns can experience more severe symptoms of anxiety and depression. Research findings are helpful for practitioners to target interventions that focus on reducing psychological inflexibility so that psychological distress can be reduced.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".