zahra malmir

 Awareness of patient rights and respecting them are both an important and a fundamental duty of healthcare staff. This study aimed to assess nurses' awareness of the patient bill of rights and their adherence to it from the perspective of hospitalized patients.

 This cross-sectional descriptive study was conducted in 2022 on 168 nurses and 383 patients admitted to Qa’em Hospital, in the city of Asadabad, Hamadan. The participants were selected using convenience sampling. The questionnaires "The Awareness of the Patient Bill of Rights" by Ruintan et al and "The Level of Compliance with Patient Bill of Rights" by Basiri Moghadam were used in this study. Data analysis was done using SPSS 18, considering a significance level of less than 0.05.

Ethical Considerations: 

In all the stages of this study, ethical principles and data confidentiality were observed and the questionnaires were completed after obtaining informed consent and explaining the research objectives to the participants.

A positive but non-significant correlation was observed between the nurses' awareness of the patient bill of rights and the level of compliance with it from the patients' perspective at a significance level of 0.05 (r=0.04, p=0.61). The mean and standard deviation of nurses' awareness of the patient bill of rights were 9.44±4.34, indicating an average level. The mean and standard deviation of compliance with the patient bill of rights in the hospital from patients' perspective were 32.90±7.73, which was considered optimal. The mean score of nurses' awareness of the patient bill of rights was higher in nurses aged 31-40 years, and this difference was statistically significant (p=0.01). There was a significant difference in the mean score of compliance with the patient bill of rights in the hospital from the perspective of patients across different age groups; the highest mean score was reported by patients over 40 years of age and the lowest, by patients aged 20-30 years (p=0.005).

 The results of the study showed a correlation between nurses' awareness of the patient bill of rights and the level of compliance with it from patients' perspective. It is necessary for the healthcare planners and policymakers to consider interventions in developing and implementing in-service training programs, providing incentives to motivate employees, and establishing strict rules to improve compliance with the patient bill of rights in hospitals. To examine the impact of factors, other than awareness, on the level of compliance with the patient bill of rights in hospitals, it is recommended that interventional and qualitative studies be conducted in the futur.

Expressive vocabulary plays a vital role in child language development, and its assessment can be one of the essential indicators to identify language developmental delay, especially in children with Down syndrome. We developed a list of expressive vocabulary and compared the size and class of expressive vocabularies between typically developing and Down syndrome children.

Expressive vocabulary of 150 children was examined in this study. A total of 120 typically developing Farsi-speaking children (in four age Groups, with a 6-month interval) and 30 children with Down syndrome (aged 24-48 months) participated in this study. The parents of the children filled out the form that included 636 words from different vocabulary classes. These classes were based on studies that investigated language development in Farsi-speaking children.

The expressive vocabulary size in Farsi-speaking children was significantly higher than in Down syndrome children (P≤0.001). There was no statistically significant difference between boys and girls regarding expressive vocabulary size in two Groups of children. The size of nouns in all age Groups is more than other classes, and the size of conjunctions in all age Groups is less than the other ones. A direct correlation was found between age and the size of expressive vocabulary.

According to the study findings, the list of expressive vocabulary can detect delays in developing expressive vocabulary.

Hospitals are One of the vital and sensitive places as well as practical that maintaining their function in normal conditions and in times of crisis is one of the biggest concerns of health system managers. By using the principles of fire safety, hospitalschr('39') vulnerability can be reduced. Fire is one of the risks that can lead to many human and financial losses as well as disruption of hospital performance. Fire risk assessment is a more effective way to assess the vulnerability, capacity and capability of the hospital. The aim of this study was to evaluate the risk of fire in one of the hospitals in Tehran to analyze the vulnerability of these uses.
This cross-sectional study was performed by using the Fire Risk Assessment Method for Engineers (FRAME) in the hospital building of a military hospital. Fire risk was calculated using the formulas of the mentioned method in EXCEL software. Factors influencing the level of risk obtained were identified and evaluated.
The level of fire risk for residents in all units was higher than one. Level factor, fire load factor and normal protection factor were identified as effective factors that result in the existing fire risk level.
The numerical value of the risk for the residents was at an unfavorable level. The results of this study showed that the use of FRAME method in assessing the risk of health facilities such as hospitals, by determining the strengths and weaknesses of the assessed locations, the possibility of identifying effective factors to predict and prevent accidents and maintain the performance of these centers at times. Be sensitive to such man-made or natural emergencies.

One of the most important issues concerning the sensor data in the Wireless Sensor Networks (WSNs) is the unexpected data which are acquired from the sensors. Today, there are numerous approaches for detecting anomalies in the WSNs, most of which are based on machine learning methods. In this research, we present a heuristic method based on the concept of “ensemble of classifiers” of data mining.  Our proposed algorithm, at first applies a fuzzy clustering approach using the well-known C-means clustering method to create the clusters. In the classification step, we created some base classifiers, each of which utilizes the data of overlapping windows to utilize the correlation among data over time by creating time-overlapped batches of data. By aggregating these batches, the classifier proceeds to find an appropriate label for future incoming instance. The concept of “Ensemble of Classifiers” with majority voting scheme has been used in order to combine the judgment of all classifiers. The results of our implementation with MATLAB toolboxes shows that the proposed majority-based ensemble learning method attains more efficiency compared to the case of the single classifier method. Our proposed method enhances the performance of the system in terms of major criteria such as False Positive Rate, True Positive Rate, False Negative Rate, True Negative Rate, Sensitivity, Specificity and also the ROC curve.