Effects of MBSR therapy on negative emotions, fatigue, and sleep quality in “post-ICU patients”
Bibliographic record
Abstract
BACKGROUND: Survivors of intensive care unit (ICU) transfer to the common ward are often accompanied by psychological distress, negative emotions, fatigue, and sleep disturbances that affect recovery. Mindfulness-based stress reduction (MBSR) has achieved reliable results in improving physical and mental health. However, no clinical study has been conducted to evaluate the effects of MBSR on negative emotions, fatigue and sleep quality of patients who survived ICU and were transferred to general wards. METHODS: This is a prospective randomized controlled trial (RCT) examining the effects of MBSR on negative emotions, fatigue, and sleep quality in inpatients transferred from ICU to general ward. Participants were randomly divided into the treatment group and the control group in a ratio of 1:1. On the basis of the same nursing plan and health education, the treatment group received MBSR therapy, while the control group received no other interventions, and all the patients were followed up for 3 months after 2 weeks of continuous treatment. The indicators included negative mood indicators [Self-rating Depression Scale (SDS) and Self-Rating Anxiety Scale (SAS)], fatigue index [Fatigue Severity Scale (FSS) and Brief Fatigue Inventory (BFI)], and sleep quality index [Pittsburgh Sleep Quality Index (PSQI)]. Finally, SPSS 20.0 software was used for statistical analysis of the data. DISCUSSION: This study will evaluate the effects of MBSR on negative emotions, fatigue, and sleep quality in hospitalized patients transferred from ICU to general ward. The results of this study will provide a reference for MBSR to improve psychological distress in ICU survivors transferred to general ward. TRIAL REGISTRATION: This study protocol was registered in the Open Science Framework (OSF) (registration number: DOI 10.17605/OSF.IO/PD7SU).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".