Evaluating the efficacy of a mindfulness mobile app for stress reduction in nurses
Bibliographic record
Abstract
Objective: All Nurses experience work stress that can take their focus away from patient care. Healthcare organizations strive to identify successful, cost-effective stress reduction programs. Mindfulness Based Stress Reduction (MBSR) training is a validated approach to stress reduction, usually in a class format. However, financial and time constraints make it inaccessible to most practicing nurses. Alternatively, mobile mindfulness apps offer an approach to mindfulness that can reach large populations, are available 24/7, anonymous, and cost effective.Methods: This prospective, study evaluated the efficacy of a mindfulness mobile app for stress reduction in nurses utilizing Whil, a Mobile App that offers mindfulness training specifically geared towards health professionals. Eight hundred and fifty-two nurses were recruited from twelve sites (71 per site) within a large Health Care System in the Northeast United States.Results: Two scales were used to test results. Nurses Stress Scale (NSS) results indicated that nurses experienced a reduction in stress level with use and time spent in the app. Nurses in the 31-40 age range and nurses on 12-hour shifts experienced greater stress levels.Conclusions: Significant differences were seen in the Subscales Conflict with Physicians, Conflict with other Nurses, and Lack of Support. There was no change in the Mindfulness Attention Awareness Scale (MAAS) over time. Spearman’s correlation showed a significant and negative correlation between NSS and MAAS scores. The Whil Mobile App is effective for stress reduction in practicing nurses on all shifts and is cost effective.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".