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Context: Kangaroo Mother Care (KMC) is a supportive technique that beings at the neonatal period and is one of the skin-to-skin contact methods of holding neonate by mother. This method has an important role in exclusive breastfeeding and thermal care of neonates. This study aimed to investigate the application of KMC and evaluate the effect of this technique in different neonatal outcomes, particularly in Iranian neonates. Moreover, this review can be a tool for formative evaluation for this newly introduced treatment intervention in Iran. Evidence Acquisition: This review was conducted in national and international databases concerning experience with KMC on term and preterm neonates admitted in Iranian hospitals from 2006 to 2014. The measured outcomes included physiologic, psychologic, and clinical effects of this practice on newborn infants. Results: In this study, 42 Persian and English language papers were reviewed and finally 26 articles were selected. Various effects of KMC on different factors such as analgesia; physiological effects, breastfeeding, icterus, length of hospitalization, infection, psychologic effects, and weight gain were found. Conclusions:The results showed that as a simple and suitable strategy for increasing the health status of the mothers and newborns, KMC had an important role in improvement of neonatal outcomes in neonatal wards of Iranian hospitals in recent ten years. Therefore, promoting this technique in all neonatal wards of the country can promote health status of this population.

Purpose. The purpose of this study is to sensitivity analysis analyze the returns to scale in two-stage network based on DEA models. The main focus of the firms has always been to obtain the maximum output with the least available resources, which points to the improvement of the firm’s performance and the importance of returns to scale and technical improvement. Design/Methodology/Approach. This study examines the sensitivity of returns to scale classifications in a two-stage DEA network. A new input-oriented model was progressed to identify the efficient decision-making units in the two-stage network, after which a new method of determining the returns to scale classifications in the efficient DMUs in two-stage network (constant, increasing, or decreasing returns to scale) was established. Findings. The stability of the returns to scale classifications in the two-stage network was analyzed. A stability region for changes in primary inputs and final outputs is only determined especially for DMUs that are efficient so that it maintains the classification of the returns to scale units. The results are shown by numerical examples. Practical Implications. The sensitivity analysis of returns to scale classifications is one of the most significant issues in data envelopment analysis (DEA), which plays an essential role in management decisions. Originality/Value. Using this model can help improve the performance of companies by using new tools and also improve the quality of work and increase acceptance competition.