Effect of telenursing on self-efficacy among myocardial infarction patients: A systematic review
Bibliographic record
Abstract
A systematic review was conducted to investigate the impact of telenursing on self-efficacy in patients who have experienced Myocardial infarction (MI). We systematically searched international electronic databases, including Scopus, PubMed, and Web of Science, from their inception to September 1, 2023, using keywords derived from Medical Subject Headings such as "telenursing", "self-efficacy", and "myocardial infarction". Furthermore, we explored Iranian databases like Iranmedex and Scientific Information Database. The quality of randomized controlled trials (RCTs) and quasi-experimental studies was assessed using the critical assessment checklist from the Joanna Briggs Institute (JBI). A total of 220 MI patients were enrolled in four studies. Of the MI patients, 67.29% were male and 50.00% were in the intervention group. The mean age of participants was 57.25 (SD=7.34) years. Of the included studies, four were quasi-experimental studies, three studies were conducted in Iran, and one study was conducted in Egypt. The mean study follow-up was 6 weeks. Also, the mean duration of the intervention was 11.67 minutes. In all studies, interventions were effective in increasing MI patients' self-efficacy. In sum, the findings demonstrated that telenursing can be effectively utilized by nurses to enhance the self-efficacy of patients. We recommend that healthcare managers and policymakers establish a platform that not only incorporates telenursing via telephone calls but also integrates other methods, such as video calls. This approach should aim to balance the costs associated with different telenursing methods, ensuring accessibility for all individuals. Consequently, in addition to traditional telephone calls, alternative telenursing modalities can be employed to enhance self-efficacy.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".