Robotic-assisted Surgery (RAS) Training Dimensions and Self-reported Clinical Competence of Nurses in a Tertiary Government Hospital in Riyadh, Saudi Arabia
Ivy A. Mejorada *
Graduate School, Wesleyan University – Philippines, Cabanatuan City, Nueva Ecija, Philippines.
Gelbert Jan Porque
Graduate School, Wesleyan University – Philippines, Cabanatuan City, Nueva Ecija, Philippines.
*Author to whom correspondence should be addressed.
Abstract
Aims: This study examined the relationships between RAS training dimensions and nurses’ self-reported clinical competence in a tertiary government hospital in Riyadh, Saudi Arabia, and descriptively evaluated perceived training effectiveness using Kirkpatrick’s Four-Level Training Evaluation Model.
Methodology: A quantitative descriptive-correlational design was used. Of 142 eligible nurses invited, 122 provided valid responses, yielding an 85.92% response rate. Data were analysed using descriptive statistics, point-biserial correlation, Spearman’s rank-order correlation, and bootstrapped multiple linear regression at α = .05. The study was conducted in the Main Operating Room of a tertiary government hospital in Riyadh, Saudi Arabia.
Results: All RAS training dimensions were rated Very High: Training Content had the highest mean (M = 4.777), followed by Hands-On Practice (M = 4.728) and Simulation-Based Activities (M = 4.696). Training effectiveness and self-reported clinical competence were also rated Very High. Sex was not significantly associated with competence (rpb = .144, p = .113), whereas age, nursing experience, perioperative experience, RAS training programs attended, and RAS cases handled showed significant positive associations (all p < .001). Training Content (ρ = .646), Simulation-Based Activities (ρ = .618), and Hands-On Practice (ρ = .717) were positively associated with competence; overall RAS training showed ρ = .755 (p < .001). The regression model explained 59.0% of the variance in self-reported clinical competence (R² = .590, p < .001), with Hands-On Practice as the only significant independent predictor (β = .510, p < .001). Findings support strengthening supervised hands-on clinical exposure while retaining theoretical instruction and simulation as complementary RAS training components.
Conclusion: Higher ratings of hands-on practice were independently associated with higher self-reported clinical competence. Findings do not establish causation or objectively assessed competence.
Keywords: Robotic-assisted surgery, perioperative nursing, hands-on training, simulation-based education, self-reported clinical competence, nursing education