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Volume 1, Issue 1 (10-2012)
Background and objective:
In the nursing profession, there are numerous factors which altogether cause occupational stress and as a result occupational exhaustion in nurses and decrease the quality of patient care. Regarding the importance of this issue which influences the health indices of the society, this study investigates the effect of progressive muscle relaxation on the occupational stress of nurses.Materials and Methods:
This semi-experimental and before-after study was conducted using progressive muscle relaxation intervention on 33 nurses in special treatment (ICU and CCU) and emergency units through simple sampling in Yazd in 2012. To assess occupational stress,Toft-Anderson questionnaire was used. The procedure of applying relaxation in a practical way was given to nurses in pamphlets and questionnaires were filled before and two weeks after the intervention. Analysis was done using SPSS.16 software and T-test.Results:
The average total score of stress in nurses before and after the intervention was determined as – 28.12±43.74 and 52.12±04.72 respectively and this difference was not statistically significant (39.0>p). However, in the dimensions of nurses’ workload (/0>p 03 and t=2.27) and patients’ suffering and death, these scores were significantly different (0001.0>p and t=3.94).Conclusion:
This study showed that applying progressive muscle relaxation technique as a method of emotion-focused coping cannot be effective in the reduction of occupational stress in nurses.Volume 2, Issue 1 (5-2014)
Background & Objective: Myocardial infarction is one of the most common coronary artery diseases. One of the educational needs of patients, is how to perform sexual activities. Unfortunately, this issue is not being taught to patients, leading to problems in patients and their partners. This study was aimed to determine the effect of sex education on patients and their spouses› satisfaction after myocardial infarction. Method: This Quasi-experimental, non-randomized study was performed on 60 patients with myocardial infarction and their spouses in the city of Yazd whom were divided into two groups of experimental and control (60 in each group),in the year 1392. The main method of this study was the education and preparation of nurses and then educating patients by trained nurses and to assess sexual satisfaction based on the standard Larson›s questionnaire. The data were then statistically analyzed using SPSS version 16 using paired, independent t-test. Results: The average sexual satisfaction of patients in the experimental group before the intervention was 81.93 ± 12.47 and after the intervention 82.50 ± 12.57 While in the control group before the intervention the average satisfaction was 83.10 ± 17.36 and after 6 weeks 75.30 ± 15.42. Also the mean sexual satisfaction of partners in the test group before and after the intervention was 81.30 ± 12.47 and 82.07 ± 12.28 respectively. In the control group before the intervention, the average score was 82.50 ± 17.21 and after intervention it was 74.57 ± 15.30. There was significant difference between patients and spouses› sexual satisfaction scores in the experimental and control groups before and after the intervention (P=0.001). Conclusion: Sex education increased the sexual satisfaction among the tested group. Therefore, it is suggested to include programs in order to prepare nurses in terms of sex education of patients and their spouses in cardiac intensive wards.
Volume 2, Issue 2 (10-2014)
Background & Objective: Prediction of health status in newborns and also identification of its affecting factors is of the utmost importance. There are different ways of prediction. In this study, effective models and patterns have been studied using decision tree algorithm. Method: This study was conducted on 1,668 childbirths in three hospitals of Shohada, Omidi and Mehr in city of Behshahr. Variables such as baby's gender, birth weight, birth order, maternal age, maternal history of illness, gestational diseases, type of delivery, reason of caesarean section, maternal age, family relationship of father and mother, mother's blood type, mother's occupation and blood pressure and place of residence were chosen as predictive factors of decision tree categorization method. The health status of the baby was used as a dependent dual-mode variable. All variables were used in clustering and correlation rules. Prediction was done and then compared using 4 decision-tree algorithms. Results: In the clustering method, the optimal number of clusters was determined as 8, using the Dunn index measurement. Among all the implemented algorithms of CART, QUEST, CHAID and C5.0, C5.0 algorithm with detection rate of 94.44% was identified as the best algorithm. By implementing the Apriori algorithm, strong correlation rules were extracted with regard to the threshold for Support and Confidence. Among the characteristics, maternal age, birth weight and reason of caesarean section with the highest impacts were found as the most important factors in the prediction. Conclusion: Due to the simple interpretation of the decision tree and understandability of the extracted rules derived from it, this model can be used for (most individuals) professionals and pregnant women at different levels.
Volume 3, Issue 2 (10-2015)
Background and Objective: Discriminant analysis and logistic regression are classical methods for classifying data in several studies. However, these models do not lead in valid results due to not meeting all necessary assumptions. The purpose of this study was to classify the number of Children Ever Born (CEB) using decision tree model in order to present an efficient method to classify demographic data.
Methods: In the present study, CART tree model with Gini splitting rule was fitted to classify the number of CEB in fertility behavior of at least once married 15-49 year-old women, in Semnan-2012. 405 women aged 15-49 years old comprised the survey sample.
Results: Women in first and second birth cohorts who had married at an early age had 3 CEB while women who had married at an older age had 2 CEB. Women in third birth cohort who had married at an early age and were employed, had 2 CEB while unemployed women in this cohort whose type of marriages were familial and non-familial had 0 and 1 CEB respectively. Women in the third birth cohort who were married in older age had 1 CEB.
Conclusion: Among important advantages of CART model are the simplicity in interpretation, using distribution-free measures, considering missing data and outliers for construction trees which has increased the usage of this method. Therefore, this method is a suitable way for classifying demographic data in comparison to other classical modeling methods in the conditions that necessary assumptions are not met.
Volume 3, Issue 2 (10-2015)
Background and objectives: Investigatingg the mortality in a population has been considered as one of the appropriate methods of health detection. Although, there are some problems such as lack of confidence in accuracy measurement and quality of data collection. Establishment of death registration systems and using international classification codes of diseases, and also mortality data integrating by responsible organizations have solved great parts of the previous problems. In this study, considering a set of parameters, the study population was divided into two groups: deceased under one year (infants) and over one year (adults). Then both groups were clustered using the K-means method to identify different groups. Hidden models and useful patterns were also discovered using decision tree algorithms. Finally, a neural network algorithm was used to show the ranking of attributes in order of their importance.
Methods: In this research, data of 12,865 deceased individuals in Golestan province since 2007 to 2009 is studied. The data has been obtained from the Health Center of Golestan province. The main characteristics used in this study are: deceased age, gender, cause of death, place of residence and place of death. K-means algorithm is used to cluster data. The decision tree algorithms and neural networks algorithm were also used for classification. Finally, results and rules were extracted. Due to different natures of causes of death in infants and adults, studying on these different groups is performed separately.
Results: In clustering phase, the optimal number of clusters is obtained by Dunn index; eight clusters for infants and seven clusters for adults were obtained. Among four decision-tree algorithms (C5.0, QUEST, CHAID and CART), C5.0 algorithm with high correction rate, 77.37% in infants data and 96.86% in adults data was the best classifier algorithm. Age, gender and place of death were the most important variables that were detected by neural network algorithm.
Conclusion: In the present study, the collected mortality data was clustered by considering the effective factors and the standard of International Classification of Diseases. The hidden patterns of mortality for infants and adults were extracted. Due to the explicit nature and the intelligibility of the decision tree algorithms, the results and extracted rules are very useful for specialists in this field.
Volume 3, Issue 2 (10-2015)
Background and Objective: Extinction coefficient is the unique value to each protein and can evaluate the type and purity of recombinant protein produced in laboratories and factories producing recombinant proteins. In this study, a simple method for calculating the extinction coefficient of recombinant protein Granulocyte-Colony Stimulating Factor (G-CSF) is presented.
Methods: G-CSF with concentration of 0.5 mg/ml was prepared from Arya Tina Gene. Sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE) method was used to check the purity of the protein, then serial dilutions of 1/2 to 1/64 of the protein were prepared and using UV-VIS spectroscopy the optical density was measured.
Results: Electrophoresis result as a single band of 18 kDa was observed. The results of UV-VIS spectroscopy of serial dilution were plotted as a distinct spectrograms and these were used to plot linear regression curve of the standard concentration of G-CSF.
Conclusion: The results of this study shows the extinction coefficient equal to 0.81 M-1 cm-1 for recombinant G-CSF that was accordant with the number reports by international producers of G-CSF and suggests the correct method is used to calculate the physicochemical parameter.
Volume 5, Issue 2 (10-2017)
Methods: This study was simulated according to the actual conditions which may exist in a city. Given the existence of a city with N × N dimensions and having several hospitals and health centers in the city, the issue was raised for the construction of three hospitals. Important factors which could influence the decision making were health status, referring times and land prices. Furthermore, the most proper locations for the construction of three hospitals were calculated using the genetic algorithm.
Results: Three characteristics including the level of health, referring times and land prices were randomly assigned to all urban areas. The coordinates of available health centers in the city were also identified. Another point was the lack of proximity of hospitals in the city. Setting the threshold of 0.2 units for the minimum distance between hospitals (current and new), this restriction was applied. After performing the algorithm with the governing conditions, three optimal points were found.
Conclusion: Considering the importance of locations for the construction of hospitals and health centers in the city and the existence of various factors for selecting the most appropriate place, application of strategies and algorithms which may be helpful in finding the best solution among the myriad of solutions in inevitable. According to the fact that human beings alone or by simple mathematical methods are not capable of taking all the features together and examine the search space to find the best result, we achieved the best solution in the city by setting the parameters of the genetic algorithm and taking into account all important factors.
Volume 6, Issue 1 (3-2018)
Background and objectives: Environmental pollution is a major cause of various diseases. Massive production of hospital, industrial, and household wastes lead to several health issues, threatening community health on a daily basis. The present study aimed to determine the association between socioeconomic status and pro-environmental behaviors in the citizens of Gorgan, Iran.
Methods: This cross-sectional, descriptive-analytical study was conducted on 400 citizens of Gorgan. Participants were divided into three regions based on urban categorization. Data were collected using demographic and socioeconomic questionnaires. To assess environmental behaviors, a standard questionnaire was used based on the Dunlap spectrum. Data analysis was performed in SPSS version 16 using the analysis of variance (ANOVA), independent samples t-test and factor analysis.
Results: No positive significant correlation was observed between gender and environmental behaviors. The results of ANOVA showed a significant association between marital status and environmental behaviors. In addition, the results of factor analysis indicated that five factors explained 55.49% of environmental behaviors. The results of ANOVA also demonstrated that middle-class citizens had a more responsible attitude toward the environment compared to others (P<0.05).
Conclusion: According to the results, socioeconomic status influenced the social value orientations and responsible behaviors of individuals toward the environment. Therefore, increasing the quality of life and providing comprehensive education could enhance pro-environmental behaviors and promote community health.
Volume 6, Issue 2 (6-2018)
Background and objectives: Birth spacing is an important variable for identification of fertility acceleration, total fertility rate, and maternal and fetal health. Therefore, special attention has been paid to this issue by researchers in the fields of medical sciences, health, and population. In addition, proper analysis of this concept is of foremost importance. Application of classical analytical techniques with no attention to their assumptions (e.g., independence of events) is associated with inefficient results. As such, this study aimed to present frailty models as effective models for this analysis.
Methods: Frailty models consider the dependence between unobserved intervals and dispersions by exerting a random impact on the model. Different types of these models include shared, conditional, correlated and time-dependent frailty, each of which along with their applications were presented in the current research using two examples. Results: In practice, the shared frailty model is highly applied due to its simplicity. Nevertheless, since most of the unknown factors affecting the birth spacing are not common between different births, the shared frailty models must be used with caution.
Conclusion: Use of classical statistical methods, such as the Cox proportional hazards model, the important assumption of which is the dependence of events occurred, is not appropriate for the accurate analysis of birth spacing. On the other hand, frailty models consider the correlation between the intervals and are an effective method for analysis of birth spacing, use of which is recommended to researchers in fields of medicine and population.
Volume 7, Issue 3 (9-2019)
Methods: A cross sectional study in 2017 was conducted by the stratified random sampling and a structured questionnaire to gather the information of 610, 15-49-year-old married women in Tehran. Considering some influential covariates on FBI, random survival forest (RSF) and conditional inference forest (CIF) were constructed by bootstrap sampling method (1000 trees) using R-language packages. Then, the best model is used to identify important predictors of FBI by variable importance (VIMP) and minimal depth measures.
Results: According to prediction accuracy results by out-of-bag (OOB) C-index and integrated Brier score (IBS), RSF outperforms CPH and CIF in analyzing FBI (C-index of 0.754 for RSF vs 0.688 for CIF and 0.524 for CPH and IBS of 0.076 for RSF vs 0.086 for CIF and 0.107 for CPH). Woman’s age was the most important predictor on FBI.
Conclusions: Applying suitable method in analyzing FBI assures the results which be used for making policies to overcome decrement in total fertility rate.
Volume 8, Issue 2 (7-2020)
Material and Methods: In this study, a two-stage deep learning-based method is presented for accurate segmentation of skin lesions. At the first stage, detection stage, an approximate location of the lesion in a dermoscopy is estimated using deep Yolo v2 network. A sub-image is cropped from the input dermoscopy by considering a margin around the estimated lesion bounding box and then resized to a predetermined normal size. DeepLab convolutional neural network is used at the second stage, segmentation stage, to extract the exact lesion area from the normalized image.
Results: A standard and well-known dataset of dermoscopic images, (ISBI) 2017 dataset, is used to evaluate the proposed method and compare it with the state-of-the-art methods. Our method achieved Jaccard value of 79.05%, which is 2.55% higher than the Jaccard of the winner of the ISIC 2017 challenge.
Conclusion: Experiments demonstrated that the proposed two-stage CNN-based lesion segmentation method outperformed other state-of-the-art methods on the well-known ISIB2017 dataset. High accuracy in detection stage is of most important. Using the detection stage based on Yolov2 before segmentation stage, DeepLab3+ structure with appropriate backbone network, data augmentation, and additional modes of input images are the main reasons of the significant improvement.
Volume 8, Issue 3 (10-2020)
Material And Methods: In this descriptive cross-sectional study, 235 medical files of patients with damages to the oromaxillofacial region available in the archive of Shahid Madani Hospital, Karaj from 2013 to 2018 were chosen as census and examined. Demographic variables including site and cause of fracture were recorded for each patient on information forms. The collected data were analyzed by SPSS 17 software and presented as descriptive statistics.
Results: In this study, out of 235 patients with oromaxillofacial fractures, 178 (75.7%) were male and 97 (41.3%) were female, respectively. The mean age of the patients was 30.96 ± 14.91 years. The main affected anatomical regions were as follows: Mandible 269 cases (49.17%), maxilla 117 cases (21.39%), and cheekbone 51 cases (9.32%). Accidents occuring with motor vehicles was the main cause of these fractures in 132 patients (56.2%).
Conclusion: The results of the present study indicated that the fractures of oromaxillofacial regions were more common in men, young people, and middle-aged individuals, and mostly occurred in the mandible, maxilla, and cheekbone, with the main cause of these fractures being accidents happening with motor vehicles.
Volume 12, Issue 4 (4-2024)
Methods: In the present research, a semi-supervised approach is proposed to predict diabetes. Improved missing value imputation (MVI) is achieved by utilizing Gaussian mixture model (GMM) clustering. The proposed classifier integrates GMM with a machine learning algorithm, specifically random forest (RF), thereby inducing a more robust predictive model via the fusion of clustering and classification techniques.
Results: The proposed method achieves an accuracy of 84%, a precision of 82.03%, a recall of 69.75%, and an F1-score of 75.12% base on experiments conducted on the PIMA Indian population.
Conclusion: Employing GMM to fill in missing values provides the advantage of replacing invalid data with the most similar records, thereby enhancing the quality of the dataset. The proposed classifier also exhibits strong predictive capabilities in identifying diabetes. By integrating this combined approach, this study offers an effective method for predicting diabetes, making a significant contribution to healthcare analytics as a whole.
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