The Relationship between Socio-Demographics and Stress Levels, Stressors, and Coping Mechanisms among Undergraduate Students at a University in Barbados
Bibliographic record
Abstract
This study sought to learn about stress experienced by students enrolled in the Faculty of Social Sciences (FSS) at the University of the West Indies (UWI) in Barbados. This research was primarily undertaken to help UWI administrators/academic staff understand and address student stress. One hundred and six FSS students responded to:- (1) student perceptions on whether summer school courses were less stressful compared to semester courses, (2) the mean stress level associated with summer and semester courses, (3) FSS student stressors, and (4) coping mechanisms used by FSS students to handle stressors. The research revealed a statistically significant difference in the mean stress levels that students experienced between summer and semester courses. The key stressors identified were: (i) amount of work in each course, (ii) group projects being a nightmare, (iii) studying and working full-time, (iv) stress associated with work impacting studies, and (v) taking too many courses per semester. The primary coping strategies used by FSS students were: (i) taking some quiet time and then resuming studies, (ii) praying for renewed strength, (iii) sleeping more, (iv) eating more, and (v) engaging in a hobby. Statistically significant results were observed on several of the key stressors and coping mechanisms. The paper concludes by discussing implications for policy and practice which can aid UWI administration/academic staff to craft strategies that can assist in reducing the amount of stress experienced by students.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".