Effect of acupressure on sleep quality among patients undergoing cardiac surgery: A systematic review
Bibliographic record
Abstract
This systematic review investigated the impact of acupressure on sleep quality among patients undergoing cardiac surgery. Major electronic databases including Scopus, PubMed, and Web of Science were systematically searched from the earliest records to November 1, 2023, using relevant Medical Subject Headings such as "acupressure", "sleep", and "cardiac surgical procedures". Additionally, Iranian databases like Iranmedex were consulted. The quality of randomized controlled trials (RCTs) and quasi-experimental studies was assessed using the Joanna Briggs Institute's (JBI) critical assessment checklist. A total of four studies involving 262 patients were included, with 60.37% being male and 53.82% assigned to the intervention group. Participants had an average age of 58.56 years (SD=8.68). The average study duration was approximately 45 weeks with a 6-day follow-up, and the typical intervention duration was 14.25 minutes. In all studies, interventions were effective in increasing sleep quality. The findings indicated that acupressure administered by healthcare professionals, such as nurses, could enhance sleep quality. The recommendation suggests that healthcare managers and policymakers create an environment in healthcare settings, that enabling nurses and other professionals to employ acupressure, thereby improving the sleep quality of patients undergoing cardiac 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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".