Background: Perioperative missed nursing care is a serious issue that can compromise patient safety and quality of care. However, little is known about the factors that influence perioperative missed nursing care. Aim: This study aimed to examine the effects of job embeddedness and polychronicity on perioperative missed nursing care as well as to test the mediating role of polychronicity on the relationship between job embeddeness and perioperative missed nursing care. Method: This was a cross-sectional correlational study that used a convenience sample of 210 operating room nurses from nine hospitals in Egypt. Data were collected using self-administered questionnaires that measured job embeddedness, polychronicity, and perioperative missed nursing care. Structural equation modeling was used to test the hypothesized relationships among the variables. Results: The findings demonstrated a significant negative and moderate association between missed perioperative care and both nurses’ job embeddedness and polychronicity. Moreover, there was a moderately positive and significant correlation between polychronicity and job embeddedness. Path analysis revealed a significant positive causal effect between job embeddedness and polychronicity. The results of mediation revealed that the indirect effect of job embeddedness on missed care through polychronicity was statistically significant; suggesting that polychronicity partially mediated this relationship. Conclusion: This study sheds light on the intricate relationship between nurses’ job embeddedness, missed care, and polychronicity in the operating theater context. By enhancing job embeddedness and fostering polychronicity among nurses, healthcare organizations can reduce perioperative missed care and ultimately improve patient care outcomes in this critical healthcare setting.

Background: The integration of artificial intelligence (AI) tools into research has brought significant advancements, enhancing efficiency, innovation, and productivity across various academic disciplines. However, alongside these transformative benefits, the growing dependence on AI tools has raised concerns regarding overreliance and the potential for addictive behaviors among researchers. Despite the widespread adoption of AI among the researchers, there remains a notable gap in the availability of validated instruments specifically designed to assess AI addiction within this context. Objective: To develop a scale to measure AI addiction among researchers and evaluate its psychometric properties. Design: A methodological design was employed, consisting of two phases: scale development and psychometric evaluation. Methods: Items were generated through a comprehensive literature review and semistructured interviews to capture AI addiction attributes. The scale’s psychometric properties—including content validity, face validity, construct validity, and internal consistency reliability—were assessed. Data from a convenience sample of 718 nursing researchers were randomly divided into two independent subsamples for exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Reliability was evaluated using Cronbach’s alpha, McDonald’s omega, split‐half reliability, and corrected item–total correlations. Results: The finalized scale comprises 22 items across five dimensions: compulsive behavior, overdependency, functional impairment, withdrawal, and tolerance. EFA identified a five‐factor structure explaining 73.66% of the variance. CFA validated the structure with robust fit indices for first order ( χ ² /DF = 2.289, CFI = 0.962, and RMSEA = 0.06) and second order ( χ ² /DF = 2.243, CFI = 0.962, and RMSEA = 0.059) models. The scale demonstrated excellent internal consistency and reliability, with Cronbach’s alpha ( α = 0.924), McDonald’s omega ( ω = 0.870), and a Spearman–Brown split‐half coefficient of 0.814. Moderate interfactor correlations ( r = 0.41–0.62) confirmed its multidimensionality. Conclusion: The researchers’ AI addiction scale is a valid and reliable tool for assessing AI addiction among researchers, providing a robust framework to evaluate compulsive behavior, dependency, functional disruption, withdrawal symptoms, and tolerance.