Evaluation of meditation and reported test anxiety in bachelor of science nursing students
Bibliographic record
Abstract
Anxiety is a growing concern among college students and often surrounds the required testing that assesses a student’s knowledge and ability to apply this knowledge to situations in nursing. If nursing students’ anxiety is not addressed, this may impact their ability to perform well on exams and be successful in their nursing program. Research supports the idea that anxiety reduction strategies can lower levels of test anxiety for those in nursing programs. The purpose of this study was to assess the level of test anxiety of students in a pre-licensure Bachelor of Science in Nursing program and to examine the use of a brief meditation video on students’ anxiety levels. A pre-test/post-test design was utilized to assess the test anxiety levels of sophomore and senior nursing students at the beginning of the semester and before their first and second exams. The results of this study found that 79.8% of the students (n = 52) reported a moderate to an extremely high level of test anxiety. In addition, there was a statistically significant (p < .000) reduction in anxiety levels at the first and second exams after implementing the meditation video compared to scores at the beginning of the semester. Based on these findings, nursing programs should consider including meditation as a strategy to reduce test anxiety in students. The program was cost-effective and would be easy to implement into a nursing program’s curriculum.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".