Quantitative Approach on Undergraduates' Student-Life Balance: Intervention for Academic Stress
Bibliographic record
Abstract
The aim of this study is to investigate the relationships between social life, academic requirements, institutional support and student life balance amongst Malaysian undergraduate students. A sample size of 200 undergraduate students from both private and public universities located in Malaysia contributed to the research data. Partial least squares structural equation modelling (PLS-SEM) is utilised to assess the influence of social life, academic requirements and institutional support on student-life balance. The results establish that social life and institutional support have a significant positive correlation to student-life balance whereas academic requirements have a significant negative correlation with student-life balance. Future research should focus on collecting qualitative data as it would provide a richer understanding that would assist universities and researchers in discovering other variables that may influence student-life balance. This study contributes to the Malaysian Education Blueprint (MEB) 2013-2025 as the current study is in line with the first objective of MEB, which is associated with student life balance and potential ways to reduce possible psychiatric symptoms among undergraduates.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".