WORK-LIFE BALANCE AS A MEDIATOR OF THE CORRELATION BETWEEN PSYCHOLOGICAL WELL-BEING AND QUARTER LIFE CRISIS IN EARLY ADULTHOOD IN TERNATE CITY
Bibliographic record
Abstract
Objective: This research is aimed at finding out whether work-life balance is a mediator of connections. psychological well-being and quarter life crisis in early adulthood in the town of Ternate. The population in this study is early adults with a vulnerable age of 18–29 years and are working. Method: This study uses correlational quantitative research methods. The sampling technique used purposive sampling technique and used the Krejcie and Morgan table as a guide to determining the sample which got a sample of 269 early adults with a vulnerable age of 18–29 years and were working. The data collection method uses a psychological scale model, namely a Likert scale adapted from previous research. Data analysis uses path analysis mediation analysis techniques using JASP software. Results: This study found that work-life balance acts as a significant mediator (partial mediation) in the connection between psychological well-being and quarter life crisis with a p value of <0.001, and an estimate value of -0.353. Individuals with good psychological well-being tend to have better work-life balance, which can reduce the level of quarter life crisis that the individual has. Novelty: This study contributes by empirically validating the mediating role of work-life balance in the relationship between psychological well-being and quarter-life crisis in early working adults, particularly within the unique socio-cultural setting of Ternate—an area that has not been widely examined in previous studies.
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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 | 0.000 | 0.001 |
| 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 | 0.002 |
| 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".