Effect of telenursing on self-efficacy among diabetic patients: A systematic review
Bibliographic record
Abstract
This systematic review aimed to investigate the impact of telenursing on self-efficacy among diabetic patients. The study searched international electronic databases, including Scopus, PubMed, and Web of Science, from the earliest available records to September 15, 2023. The search used keywords derived from Medical Subject Headings, specifically "telenursing", "self-efficacy", and "diabetes mellitus". Iranian databases such as Iranmedex and the Scientific Information Database were also consulted. The quality assessment of included studies, which consisted of randomized controlled trials (RCTs) and quasi-experimental studies, was performed using the Joanna Briggs Institute's (JBI) critical assessment checklist. The review included a total of five studies involving 478 diabetic patients. Among these patients, 74.32% were female, and 52.00% were allocated to the intervention group. The average age of the participants was 48.76 (SD=7.48) years. The mean duration of the studies and their respective follow-up periods were 32.80 and 12.80 weeks, respectively. Furthermore, the average duration of the telenursing intervention was 23.33 minutes. The findings of this review indicated that telenursing can be an effective method for enhancing patient self-efficacy. The study suggests that healthcare managers and policymakers should consider establishing a platform incorporating telenursing via phone calls and alternative methods like video calls. This approach should aim to balance the costs associated with these different methods to ensure accessibility for all individuals. Consequently, other modalities can be integrated alongside traditional telephone-based telenursing to promote 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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".