Comparative Analysis of Stress Levels Among Undergraduate Students at St. John's College, Agra
Bibliographic record
Abstract
Background Stress is prevalent among college students, impacting their mental health and academic performance. Understanding the distribution and determinants of stress levels in students is crucial for developing effective interventions. This study aims to assess the prevalence of stress and its association with sociodemographic factors among undergraduate students at St. John's College, Agra, India. Materials and methods A descriptive cross-sectional study was conducted between August 1, 2023, and December 31, 2023, involving 160 undergraduate students from B.A., B. Com., and B.Sc. programs. Students were selected using a stratified random sampling technique. Stress levels were measured using the Perceived Stress Scale (PSS), which classifies low, moderate, and high levels. Data were collected through a validated, semi-structured questionnaire administered via Google Forms. Statistical analysis was performed using IBM SPSS Statistics for Windows, Version 29 (Released 2023; IBM Corp., Armonk, New York), with descriptive statistics and chi-square tests used to examine associations between stress levels and sociodemographic variables. Results The study found that 103 (64.4%) of students experienced moderate stress, 34 (21.3%) reported high perceived stress, and 23 (14.4%) had low stress. A significant association was observed between stress levels and gender (p = 0.022), with female students more likely to experience high stress. Additionally, urban students reported higher stress levels than their rural counterparts (p = 0.012). However, no significant differences in stress levels were found across different courses. Conclusion The study reveals a substantial prevalence of moderate to high stress among college students, particularly among females and those from urban areas. These findings suggest the need for targeted stress management interventions to support student well-being. Further research is recommended to explore the underlying causes of stress and develop comprehensive stress reduction strategies in the student population.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".