The Big Five Personality Traits as Predictors of Negative Emotional States in University Students in Taiwan
Bibliographic record
Abstract
Negative emotional states, such as stress, anxiety, and depression, are prevalent in university students. Personality traits have been shown to be associated with a wide range of behaviors in students, such as academic motivation, achievement, and social well-being. The aim of this study was to investigate the association between the Big Five personality traits and negative emotion states in university students in Taiwan. A cross-sectional study was conducted on 580 university students in Taiwan. Negative emotional states were evaluated using the Depression Anxiety Stress Scale-21 (DASS-21) and the Big Five personality traits were measured using the 48-item Big Five Inventory. A hierarchical linear regression analysis was used to assess the factors associated with DASS-21 scores. Neuroticism (standardized beta [std. β] = 0.45, p < 0.001) and openness (std. β = 0.12, p = 0.003) were significantly associated with DASS-21 scores, while agreeableness (std. β = −0.10, p = 0.007) was significantly and inversely associated with DASS-21 scores. Personality traits could be used to identify students at risk of negative emotional states and to undertake appropriate preventive strategies.
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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".