Prioritizing Public- Private Partnership Models for Public Hospitals of Iran Based on Performance Indicators

Background: The present study was conducted to scrutinize Public- Private Partnership (PPP) models in public hospitals of different countries based on performance indicators in order to se-lect appropriated models for Iran hospitals.Methods: In this mixed (quantitative-qualitative) study, systematic review and expert panel hasbeen done to identify varied models of PPP as well as performance indicators. In the second stepwe prioritized performance indicator and PPP models based on selected performance indicatorsby Analytical Hierarchy process (AHP) technique. The data were analyzed by Excel 2007 andExpert Choice11 software’s.Results: In quality – effectiveness area, indicators like the rate of hospital infections(100%), hospital accidents prevalence rate (73%), pure rate of hospital mortality (63%), patientsatisfaction percentage (53%), in accessibility equity area indicators such as average inpatientwaiting time (100%) and average outpatient waiting time (74%), and in financial – efficiency area,indicators including average length of stay (100%), bed occupation ratio (99%), specific incometo total cost ratio (97%) have been chosen to be the most key performance indicators. In the prioritizationof the PPP models clinical outsourcing, management, privatization, BOO (build, own,operate) and non-clinical outsourcing models, achieved high priority for various performance indicatorareas.Conclusion: This study had been provided the most common PPP options in the field of public hospitals and had gathered suitable evidences from experts for choosing appropriate PPP option for public hospitals. Effect of private sector presence in public hospital performance, based on which PPP options undertaken, will be different

Inhaled Lavender Effect on Anxiety and Pain Caused From Intrauterine Device Insertion

Introduction: Intrauterine device (IUD) is the most common reliable, effective and reversible contraceptive method used worldwide and in areas with high growth rate is of particular importance. IUD insertion is associated with high anxiety in most people that causes pain and discomfort. The aim of this study was to determine the effects of aromatherapy on anxiety and pain caused by IUD insertion. Methods: This study was conducted on 106 women in a health care center located in Ardebil, Iran. Participants were divided into two groups by randomized blocks of 4 and 6. In the experimental group lavender scent was inhaled and in the control group the placebo was inhaled 30 minutes before IUD insertion. The anxiety of the participants was measured by Spielberger questionnaire, and the pain of IUD insertion was measured immediately after the insertion using visual analog scale (range 0-10). Results: The mean score (standard deviation) of anxiety before intervention was 43.2 (9.2) in the experimental group that decreased after intervention to 39.0 (10.5) (p < 0.001), while this score was 42.2 (9.0) and 41.5 (8.4) before and after the intervention in the control group (p = 0.21). Mean differences of anxiety in both groups was statistically significant (p < 0.001). The pain score after intervention did not show significant difference between two groups (p = 0.51). Conclusion: Aromatherapy with lavender inhalation was effective in decreasing anxiety in IUD procedure, and this method can be used in health care centers as complementary treatments

Multiple Classifier Strategies for Dynamic Physiological and Biomechanical Signals

Access technologies often deal with the classification of several physiological and biomechanical signals. In most previous studies involving access technologies, a single classifier has been trained. Despite reported success of these single classifiers, classification accuracies are often below clinically viable levels. One approach to improve upon the performance of these classifiers is to utilize the state of- the-art multiple classifier systems (MCS). Because MCS invoke more than one classifier, more information can be exploited from the signals, potentially leading to higher classification performance than that achievable with single classifiers. Moreover, by decreasing the feature space dimensionality of each classifier, the speed of the system can be increased. MCSs may combine classifiers on three levels: abstract, rank, or measurement level. Among them, abstract-level MCSs have been the most widely applied in the literature given the flexibility of the abstract level output, i.e., class labels may be derived from any type of classifier and outputs from multiple classifiers, each designed within a different context, can be easily combined. In this thesis, we develop two new abstract-level MCSs based on "reputation" values of individual classifiers: the static reputation-based algorithm (SRB) and the dynamic reputation-based algorithm (DRB). In SRB, each individual classifier is applied to a “validation set”, which is disjoint from training and test sets, to estimate its reputation value. Then, each individual classifier is assigned a weight proportional to its reputation value. Finally, the total decision of the classification system is computed using Bayes rule. We have applied this method to the problem of dysphagia detection in adults with neurogenic swallowing difficulties. The aim was to discriminate between safe and unsafe swallows. The weighted classification accuracy exceeded 85% and, because of its high sensitivity, the SRB approach was deemed suitable for screening purposes. In the next step of this dissertation, I analyzed the SRB algorithm mathematically and examined its asymptotic behavior. Specifically, I contrasted the SRB performance against that of majority voting, the benchmark abstract-level MCS, in the presence of different types of noise. In the second phase of this thesis, I exploited the idea of the Dirichlet reputation system to develop a new MCS method, the dynamic reputation-based algorithm, which is suitable for the classification of non-stationary signals. In this method, the reputation of each classifier is updated dynamically whenever a new sample is classified. At any point in time, a classifier’s reputation reflects the classifier’s performance on both the validation and the test sets. Therefore, the effect of random high-performance of weak classifiers is appropriately moderated and likewise, the effect of a poorly performing individual classifier is mitigated as its reputation value, and hence overall influence on the final decision is diminished. We applied DRB to the challenging problem of discerning physiological responses from nonverbal youth with severe disabilities. The promising experimental results encourage further development of reputation-based multi-classifier systems in the domain of access technology research.Ph

From Attachment to a Sacred Figure to Loyalty to a Sacred Route: The Walking Pilgrimage of Arbaeen

Around 20 million Shia pilgrims shape one of the world&rsquo;s biggest pilgrimages in Iraq, called &ldquo;Arbaeen,&rdquo; many of whom walk long distances to Karbala city as a part of the ritual every year. Faith in Imam Hussein, who was martyred in the battle of Karbala in 680 CE, is central among all pilgrims in this ritual, but the main question is how do the pilgrims&rsquo; faith and psychological cognitions translate into this spiritual journey with different meanings during the Arbaeen pilgrimage? The present study aims to discover the different social and psychological reasons for pilgrims&rsquo; feelings of attachment to Imam Hussein and to the Arbaeen pilgrimage route. Through 57 semi-structured in-depth interviews with pilgrims in two phases, Arbaeen 2014 and 2019, four different perceived roles for Imam Hussein including beloved, interceding, transformative, and unifier figure were found, leading pilgrims to feel an attachment to him. The current study mainly contributes to the literature by presenting an empirical analysis of Muslims&rsquo; experiences and perceptions of Islamic theology, and their loyalty to a sacred route through attachment to a sacred figure

The protective effect of vitamin C on growth, digestive enzymes, immune response, and gill histology in Caspian roach (Rutilus rutilus caspicus) under diazinon stress

In recent years, the increase in the use of agricultural pesticides has endangered the production of marine creatures. However, exposing farmed creatures to water contaminated with pesticides is one of the current challenges. This study aims to investigate the defensive impact of diets containing varying amounts of vitamin C on the growth, digestive enzyme activity, immune response, and resistance of Caspian roach (Rutilus rutilus caspicus) exposed to diazinon. Fish (average weight: 1.12 ± 0.17 g) were fed diets containing 0 (control; C0), 500 (C1), 1000 (C2), and 1500 (C3) mg/kg of vitamin C for 56 days. Then they were exposed to diazinon stress for 14 days (0.32 mg/liter). The highest final weight and lowest feed conversion ratio were obtained in fish fed a diet containing vitamin C. The highest protease activity and lowest amounts of lipase and amylase were observed in the C3 group. At the end of the feeding period, lysozyme activity, total immunoglobulin amount, and complement (ACH50) in the C3 treatment were significantly higher than in the control treatment. After the stress period, lysozyme and complement activity were higher in all groups fed vitamin C supplements than in the control treatment. The highest levels of stress indicators (cortisol and alkaline phosphatase) were detected in the control group before and after stress. Based on the preparation of gill tissue sections, most lesions included edema, hyperplasia, adhesion of secondary lamellae, and necrosis of gill-covering cells. Overall, the results indicate that adding vitamin C to the diet can be used as a sustainable bioremediation tactic and reduce the adverse effects of diazinon exposure in Caspian roach