A quarter-life crisis in early adulthood in Indonesia during the Covid-19 pandemic
Bibliographic record
Abstract
Quarter-life crisis in youngsters in their 20s triggered by concerns over uncertainties of future life, notably regarding job prospects, romantic relationships linked with marriage plan, and social life. The purpose of this study is to understand the psychosocial dynamics of the quarter-life crisis and to comprehend the impact of the Covid-19 pandemic on the quarter-life crisis among early adults in various regions in Indonesia. The research uses a phenomenological method of qualitative approach. Research participants were selected using the purposive sampling technique, composed of 6 people who experienced a quarter-life crisis. The age range of the participants was 20 to 29 years. Semi-structured interviews through Whatsapp media were used to collect the data. The data were analyzed with Interpretative Phenomenological Analysis (IPA). The results showed that work-related demands, marriage plans, and family-related issues are the root causes of quarter-life crises marked by disturbing negative thoughts and feelings. Pandemic Covid-19 has also intensified the anxieties felt by some participants as the economic situation and job prospects got even bleaker. This study implies that to prevent a quarter-life crisis among youth, families are expected to be more supportive by providing support and trust for young adults to make decisions and be responsible for their choices for the future. Keywords: content validity, confirmatory factor analysis, interreligious harmony.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| 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".