Role of Emotional Maturity and Social Support in Predicting Quarter-Life Crisis in Emerging Adulthood Using Multiple Linear Regression Analysis
Bibliographic record
Abstract
This study aims to examine the role of emotional maturity and social support in predicting the level of quarter-life crisis in emerging adulthood. The employed research method was multiple linear regression analysis. The participants were individuals aged 18 to 29 years. Further, 122 participants were selected using convenience sampling. The data were collected using a questionnaire survey based on the Multidimensional Scale of Perceived Social Support to assess social support and the quarter-life crisis scale based on the theory by Robbins and Wilner. To assess emotional maturity, we used the emotional maturity scale based on the theory by Walgito. Emotional maturity and social support were important in predicting the level of quarter-life crisis in emerging adulthood. Higher levels of emotional maturity and social support were associated with lower levels of quarter-life crisis experiences in emerging adulthood.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".