Educating psychiatric mental health didactic and clinical nursing students on mindfulness activities utilizing the calm mobile application
Bibliographic record
Abstract
Background and aim: Stress can develop when an individual becomes overwhelmed with appointed tasks and nursing students are not exempt from this reaction when experiencing academic pressure. Nursing students can learn appropriate ways to decrease stress through coping strategies. The study aimed to educate nursing students about the coping strategy mindfulness through the utilization of the Calm mobile application (Calm), decreasing stress, and increasing the awareness and utilization of mindfulness.Methods: The purpose of the Quality Improvement (QI) project was to educate nursing students on mindfulness as a coping strategy through supplemental resource videos and the usage of Calm. Students completed a pre-/post-survey modeled after the Mindful Attention Awareness Scale with an addition of three qualitative questions.Results: At the start of the project, 99% of students practiced mindfulness through Calm, 0-15 minutes a week while at the end of the project, 46% of students practiced mindfulness through Calm, 15-60 minutes a week. Pre-project implementation, 19% of students agreed that Calm is useful in the practice of mindfulness while post-project implementation, 44% of students agreed that Calm is useful. A Paired t-test was conducted regarding mindfulness awareness and showed 95% confidence with a pre-mean of 3.2 and a post-mean of 3.6. A difference of 0.4 between both pre-post-means resulting in a significant increase after intervention.Conclusions: The QI project was successful in increasing the awareness and utilization of mindfulness and in educating nursing students about the coping strategy mindfulness which can decrease stress.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".