Dinamika Permasalahan Psikososial Masa Quarter Life Crisis Pada Mahasiswa
Bibliographic record
Abstract
Each student show a different response to serve in stages of human development early adulthood. If student don’t serve well, a student will experience quarter life crisis which also causes psychosocial problems arise. The purpose of this study was to determine the dinamic of psychosocial problems in individual facing quarter life crisis. This study uses a qualitative method with a case study approach. The number of participants wa three people and three informants who were close friends of the participants. Data were collected using semi-structures interviews and observations made during the interview process as a complement. The data were analyzed by data reduction, data display, and conclusion drawing. The results show that the ynamics of psychosocial problems in students who are facing a quarter-life crisis begins with the existence of a situation or event encountered that disrupts the psychosocial condition of the participants. Then the participant will try to survive and live the condition until finally the participant cannot do anything. Furthermore, psychosocial problems in participants who face a quarter life crisis have a negative impact both physically and mentally. Responses or ways to deal with psychosocial problems for each participant varied from sleeping, social withdrawal, to smoking. However, the participants continued to live their lives according to their abilities.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".