JIVA: Burnout Early Detection and Mindfulness Therapy for College Students to Prevent Self-diagnosis in the Quarter Life Crisis Era
Bibliographic record
Abstract
Burnout has harmed students. Recent ventures, such as counseling, have yet to provide significant benefits. Furthermore, there is a tendency for college students to self-diagnose. However, self-diagnosis often leads to misdiagnosis and mishandling, triggering more severe health problems. This study aims to know if Jiva can be used as burnout early detection and mindfulness therapy for college students to prevent self-diagnosis in the quarter-life crisis era. The research was conducted at Marcopolo Bali International from October-December 2022. Population of this study was eight respondents for the small group trial and 80 respondents for the large group trial. The research method used is Research & Development with ADDIE research design. Qualitative and quantitative data will be analyzed descriptively. Jiva app is software that integrates Burnout Early Detection and Body Scan Meditation. The creation of the application will help find burnout as early as possible and treat student burnout. The results of the material validation test showed that the application was included to excellent category with the score of 4.75 (95%). The results of the media validation test showed that the application was included to excellent category with the score of 4.32 (86%). Furthermore, the small group trial pointed to the application belonging to good category with the score of 3.95 (79%). The large group trial pointed to good category with the score of 3.9 (78%). Thus, Jiva can be used as burnout early detection and mindfulness therapy for college students to prevent self-diagnosis in the quarter-life crisis era.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".