The influence of time management skills on stress and academic performance level among nursing students
Bibliographic record
Abstract
College students need to possess many skills to successfully pass each year of study. Effective time management skills are an essential characteristic for college students to have in order to maintain good academic performance. The students’ ability to manage their time properly is linked to many positive consequences, such as higher academic performance and lower stress levels. Despite the importance of time management skills for students, a limited number of studies were conducted to examine this issue among students in Saudi Arabia, especially nursing students. Therefore, the aim of this study was to explore the influence of time management skills on stress and academic performance levels of nursing students in Saudi Arabia. In this quantitative research, a cross-sectional design was used to explore the relationship between the level of time management skills and stress level of nursing students. A total of 150 students completed a survey (a response rate of 65%). The two scales included in this survey were the Student Nurse Stress Index and Time Management Questionnaire. Pearson’s correlation coefficient showed that there was a positive and significant correlation between the time management skills and academic performance of the students. That is, the increasing level of time management skills was associated with increasing academic performance (r = .240, p < .003), there was a positive and significant correlation between the time management skills and year of study (r = .262, p < .001), and there was a positive and significant correlation between the stress level and year of study (r = .249, p < .002). Therefore, time management and stress management training programs should be provided for nursing students during the orientation period.
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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 | 0.004 |
| 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 | 0.001 | 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".