Self-Esteem and Attachment as Predictors of Resilience in Early Adults Experiencing Quarter-Life Crisis
Bibliographic record
Abstract
Resilience is the individual's ability to choose to recover from sad and challenging life events by increasing their knowledge to be adaptive and overcome similar adverse situations in the future. In early adulthood, many individuals experience a condition called quarter-life crisis, where they feel a sense of worry caused by uncertainty about their future life. Therefore, resilience is needed to face the existing challenges, in order to be able to adapt and protect individuals from the rigors of stress. Resilience arises as a protective factor that is distinguished internally and externally. Externally, resilience is related to attachment, which is a continuous affective bond characterized by a tendency to seek and maintain closeness to specific figures, especially when under pressure. Therefore, the purpose of this research is to determine the influence of self-esteem and attachment as predictors of resilience in young adults who are experiencing quarter-life crisis simultaneously. The method in this research is quantitative, with a sample of early adults experiencing quarter-life crisis, thus the sampling technique used in this research is incidental sampling. The data collection technique used a questionnaire with a resilience scale, the standardized Rosenberg Self-Esteem Scale (RSES), and the attachment scale using the Experiences in Close Relationship-Revised-General Short Form (ECR-R-GSF) scale, Additionally, the Developmental Crisis Questionnaire (DQC-12) scale was used to measure quarter-life crisis in early adults. The data analysis technique used in this research is multiple regression analysis. From the research results using SPSS version 27, a significant value was obtained for both independent variables of 0.000 < 0.005, therefore Ha is accepted and H0 is rejected, indicating that self-esteem and attachment together are predictors of resilience in early adults experiencing quarter-life crisis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".