Improving sleep after open heart surgery–Effectiveness of nursing interventions
Bibliographic record
Abstract
Background and Objective : Cardiac surgical patients experience sleep problems in the early post-operative period and after hospital discharged. Restorative sleep is important to be able to handle the challenges of rehabilitation, but often remains untreated. Pharmacological treatment has been preferred, but studies conclude a longer lasting effect of cognitive behavioural therapy (CBT). Few clinical trials have been conducted on nurse led sleep promoting interventions during hospitalization. The hypothesis of this study is that systematic training and education in sleep, sleep anamneses and sleep hygiene enhances nurses ’ awareness on sleep problems, and as a result makes nurses able to propose appropriate interventions to improve patients’ sleep during hospitalization, and after discharged. The aim is to examine the effect on patients’ self-reported sleep quality. Methods : The study design is a controlled intervention study. Patients in the control group received usual care. Patients in the intervention group received nursing focused on improving sleep by use of sleep-anamneses and sleep hygienic principles. Patients ’ sleep-quality was measured preoperatively, and one and two month post-operatively by use of PSQI-questionnaire and sleep diaries. Results : There was no significant effect of the intervention, though there were several signs that had some effect after two months in terms of global PSQI, total sleep time, sleep efficiency, sleep medication and sleep quality. Conclusions : Systematic education and training of nurses in sleep, sleep anamneses and sleep hygienic principles has some effect on patients self-reported sleep quality two months after heart surgery.
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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) | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".