Evaluation of a Simulation Program for Providing Telenursing Training to Nursing Students: Cohort Study
Bibliographic record
Abstract
Background: Telenursing has become prevalent in providing care to diverse populations experiencing different health conditions both in Israel and globally. The nurse-patient relationship aims to improve the condition of individuals requiring health services. objectives: This study aims to evaluate nursing graduates' skills and knowledge regarding remote nursing care prior to and following a simulation-based telenursing training program in an undergraduate nursing degree. Methods: A cohort study assessed 114 third-year nursing students using comprehensive evaluation measures of knowledge, skills, attitudes, self-efficacy, and clinical skills regarding remote nursing care. Assessments were conducted at 2 critical time points: prior to and following a structured simulation-based training intervention. Results: Participant demographics revealed a predominantly female sample (101/114, 88.6%), aged 20-50 years (mean 25.68, SD 4.59 years), with moderate to advanced computer and internet proficiency. Notably, 91.2% (104/114) had no telenursing exposure, yet 75.4% (86/114) expressed training interest. Statistical analyses demonstrated significant improvements across all measured variables, characterized by moderate to high effect sizes. Key findings included substantial increases in telenursing awareness, knowledge, skills, attitudes and self-efficacy; significant reduction in perceived barriers to remote care delivery; and complex interrelation dynamics between variables. A multivariate analysis revealed nuanced correlations: higher awareness and knowledge were consistently associated with more positive attitudes and increased self-efficacy. Positive attitudes correlated with enhanced self-efficacy and reduced perceived barriers. Change score analyses further indicated that increased awareness and knowledge facilitated more positive attitudinal shifts, while heightened awareness and positive attitudes corresponded with decreased implementation barriers. Conclusions: The study underscores the critical importance of integrating targeted telenursing training into nursing education. By providing comprehensive preparation, educational programs can equip students to deliver optimal remote care services. The COVID-19 pandemic has definitively demonstrated that remote nursing will be central to future health care delivery, emphasizing the urgent need to prepare nursing students for this emerging health care paradigm.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".