Educational approaches for patients with heart surgery: a systematic review of main features and effects
Bibliographic record
Abstract
INTRODUCTION: Patients who undergo heart surgery are exposed to mental and physical difficulties after discharge from hospital. They often need support and follow-up after discharge. The use of educational approaches or solutions before or after heart surgery can increase patients' knowledge on the post-operative complications and self-care. The main purpose of this systematic review is to determine the applications of educational approaches and investigate the effects of these approaches on patients with heart surgery. METHOD AND MATERIEL: A thorough search was conducted in Medline (through PubMed), Scopus, ISI web of science to select related articles published between 2011 and May 2022. All of the retrieved papers were screened according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. RESULTS: A total of 29 articles were obtained from the search, which included in this systematic review after being assessed based on inclusion and exclusion criteria. Most of the articles (n = 10, 34.48%) had been conducted in Canada and Iran, with the most significant number published in 2016. Out of 29 studies, 23 were experimental studies, and six were observational-analytical studies. The number of participants in the studies ranged from 11 to 600 (IQR1: 57.5, median: 88, IQR3: 190). In 28 (96.55%) studies, the educational approaches had a significant effect on clinical, economic or patient-reported outcomes. The greatest effect reported by the studies was related to clinical outcomes such as patient care improvement or change in clinical practice. The most effects in the patient-reported outcomes were related to improving patient satisfaction and patient knowledge. In terms of global rating scores, 17.24% of the included studies were considered as weak, 20.68% as moderate, and 62.06% as strong. CONCLUSION: The results of systematic review showed that the use of educational approaches by patients before and after heart surgery can have significant effects on reducing stress and financial burden, and increasing the quality of care and level of knowledge in patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".