HUBUNGAN SELF-COMPASSION DENGAN KRISIS SEPEREMPAT KEHIDUPAN PADA EMERGING ADULTHOOD PENGGUNA AKTIF INSTAGRAM
Bibliographic record
Abstract
Quarter life crisis is a problem that many emerging adults experience, especially along with the massive use of Instagram as a medium to display a certain measure of success. Self-compassion is a solution to overcome crises related to social media which is influential in encouraging social media users not to judge themselves. This study aims to determine the relationship between self-compassion and quarter-life crisis in emerging adults who actively use Instagram. The method used was quantitative correlation with a total of 236 participants consisting of 72 men and 164 women in emerging adulthood on the island of Java. The research sample was obtained by using quota sampling technique. The measuring instruments in this study were the self-compassion scale and the quarter-life crisis scale, then analyzed using the Pearson product moment correlation test. The results of the study showed that there was a negative relationship between self-compassion and a quarter of life crisis in emerging adults who were active Instagram users with a significance value of 0.000 (p<0.05). The effective contribution of the self-compassion variable to a quarter of life crisis is 30% and the isolation aspect contributes the most by 17.75%. In this study there was no difference in the level of self-compassion and quarter of life crisis for men and women. These results provide a theoretical implication that a quarter life crisis can be reduced when individuals have high self-compassion.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".