Adversity intelligence, family support, and quarter life crisis in individuals who married at a young age
Bibliographic record
Abstract
The phenomenon of early marriage is characterized by various challenges caused by various factors, one of which is psychological unpreparedness. Getting married at a young age is closely related to the problem of quarter life crisis (QLC). QLC is an emotional crisis that occurs at the age of 18-29 years who experience unstable conditions such as indecisiveness, a variety of choices, and even feelings of giving up easily. QLC is influenced by internal and external factors, in this research it focuses on adversity intelligence (internal) and family support (external). This study aims to analyze the relationship between adversity intelligence and family support with QLC in individuals who marry at a young age. This research uses quantitative methods with a sampling technique, namely purposive sampling. The subjects of this research were 50 individuals who married at an early age. Data were collected using the quarter life crisis scale, adversity intelligence scale and family support scale which were analyzed using multiple regression analysis with the help of JASP 16.2 for Windows software. The research results show a value of r = 0.762 with p < 0.001, meaning that there is a significant relationship between the variables of adversity intelligence and family support and the quarter life crisis in individuals who marry at a young age with an effective contribution of 58%. Based on the results of this research, suggestions for further research are expected to control personality traits in reviewing the QLC that occurs in individuals who marry at a young age.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".