Prompt attention of the diagnostic medical imaging centers in Tehran: An experience of people with physical disability

Background: Nowadays, change in the pattern of diseases to chronic has been with increasing use of medical imaging services. People with physical disability require continuous diagnostic and therapeutic services. Objectives: This study aimed to measure the waiting time for receiving medical imaging services and to examine sociodemographic factors associated with the poor experience of these people. Patients and Methods: In a cross-sectional study to assess the responsiveness of comprehensive rehabilitation centers in Tehran in 2016-2017, 610 people with physical disabilities who were referred to 10 comprehensive physical rehabilitation centers for rehabilitation services were asked whether they had used medical imaging services during their rehabilitation. The 477 participants (218 women) with positive response consisted the sample of this study. A checklist was used for data collection. T test, Chi-Square and Multiple Logistic Regression Model were used for analytical reports. Results: Poor experience in prompt attention of centers was reported by 26 of public diagnostic service users while 16 of private sector users and 21.2 by total sample. Waiting time for appointment and waiting time at the center were significantly longer in public medical imaging centers compared to the private sector (P < 0.05). Overall experience of public service users about prompt attention was poorer than the private sector (P < 0.05). Physical status (odd ratio OR = 3.2; 95% confidence interval CI = 1.3-7.8) and duration of disability (OR = 0.28; 95% CI = 0.09-0.9) were the predictors of poor experience of respondents about prompt attention in public and private centers respectively. Conclusion: From the service users� viewpoint, private centers had better performance in prompt attention than public centers. Attention to physical condition and duration of disability in scheduling diagnostic services is recommended. © 2018, Author(s)

Predictors of poor responsiveness in physical rehabilitation centers in Tehran

Background: Responsiveness as a nonmedical, nonfinancial aspect of a health system's goals requires special attention, particularly in people with physical disabilities. This study aimed to investigate the predictors of poor responsiveness of rehabilitation centers in Tehran. Methods: A cross sectional study was conducted to investigate 610 individuals with physical disabilities who referred to 10 comprehensive rehabilitation centers in Tehran using Quota sampling in 2016-2017. The following questionnaires were used in this study: Health System Responsiveness questionnaire, recommended by World Health Organization (WHO); Activities of Daily Living (ADL); and Instrumental Activity of Daily Living (IADL). Multiple logistic regression models were used to determine the sociodemographic characteristics (sex, age, perceived social class, etc.), self-assessed health, and physical functioning (eg, Instrumental Activities of Daily Living (IADL) as predictors of poor responsiveness in comprehensive rehabilitation centers of Tehran. Results: The mean years of education of respondents was 12.57 (SD=5.07). The majority of the participants perceived themselves as belonging to the middle class. Among the participants, 17.1% were completely dependent in their instrumental activities of daily living (IADL). Respondents who were not satisfied with their health insurance accounted for 40.2% of the sample. Also, 20.9% of the participants reported poor responsiveness. Based on the logistic regression model, variables of education, perceived social class, satisfaction with health insurance, and IADL were predictors of overall poor responsiveness after adjusting other covariates. Conclusion: Level of education was a strong predictor of poor responsiveness. Insurance companies should make policies to facilitate people's access to rehabilitation services and increase customer satisfaction. Moreover, rehabilitation service providers should pay special attention to those with physical disabilities who are more severely disadvantaged. © Iran University of Medical Sciences

An explanatory model of depression among female patients in Fars, Kurds, Turks ethnic groups of Iran

Background: Depressive disorder is globally estimated to be as many as one in five visits to primary health care. Approximately more than 50 of depressed women in primary care are not diagnosed. As a part of a major investigation into perceptions of women's depression, this study explored how female patients and their relatives conceptualize patients' conditions in three ethnic groups in Iran (Fars, Kurds and Turks). Methods: Qualitative methods were used for data collection. Depressed women and their relatives were purposively selected from the public psychiatric clinics affiliated to university of medical sciences in the three study cities. Twentyfive depressed women and 14 relatives were interviewed in three ethnic groups. Results: One theme "illness meaning", including three categories: perceived symptoms, label of the illness, and effects of the illness was found through the content analysis. The participants perceived symptoms of illness as somatic and psychological depending on the participant's assumed reason for the onset of the illness. There were most similarities in term used for of the illness in the three ethnic groups. Most of the study participants described the illness in terms of nerve problems/illness, and depression "afsordehgi". The most important effects that depressed women had experienced because of their illness were marital conflict or a guilt feeling originating from their inability to support family. Conclusion: These findings suggest the need to recognize and choose appropriate diagnostic approach for depressed women in the context of Iran

Local perceptions of mental health in Iran, Semnan Province

Introduction: Understanding local perceptions of mental health in different cultures and contexts is crucial for designing and implementing appropriate mental healthcare services. Methods: This qualitative study was conducted to investigate local perceptions of mental health in two highly populated provincial districts in Iran. Data were collected using the free list technique and interviews. A two-phase training workshop was held with the research team at a local health center, followed by a pilot study with the participation of six subjects. All the interviews were audio-recorded, transcribed, and then analyzed by the third and fourth authors in DEDOOSE. Results: A total of 30 individuals (20 in the free list and 10 as key informants in the interviews) took part in the study. Based on the study findings and the key informants' ideas, mental health problems were categorized into three categories of depression, anxiety, and obsessive�compulsive disorder (OCD). Conclusions: Mental health problems appear to be expressed in different ways and with different symptoms in different cultures, and there is a distinct need for examining mental disorders in each culture and nationality separately using culturally appropriate tools for disease screening. © 2020 The Authors. Brain and Behavior published by Wiley Periodicals LL

Detecting Distributed Denial of Service Attacks in Neighbour Discovery Protocol Using Machine Learning Algorithm Based on Streams Representation

© 2018, Springer International Publishing AG, part of Springer Nature. The main protocol of the Internet protocol version 6 suites is the neighbour discovery protocol, which is geared towards substitution of address resolution protocol, router discovery, and function redirection in Internet protocol version 4. Internet protocol version 6 nodes employ neighbour discovery protocol to detect linked hosts and routers in Internet protocol version 6 network without the dependence on dynamic host configuration protocol server, which has earned the neighbour discovery protocol the title of the stateless protocol. The authentication process of the neighbour discovery protocol exhibits weaknesses that make this protocol vulnerable to attacks. Denial of service attacks can be triggered by a malicious host through the introduction of spoofed addresses in neighbour discovery protocol messages. Internet version 6 protocols are not well supported by Network Intrusion Detection System as is the case with Internet Protocol version 4 protocols. Several data mining techniques have been introduced to improve the classification mechanism of Intrusion detection system. In addition, extensive researches indicated that there is no Intrusion Detection system for Internet Protocol version 6 using advanced machine-learning techniques toward distributed denial of service attacks. This paper aims to detect Distributed Denial of Service attacks of the Neighbour Discovery protocol using machine-learning techniques, due to the severity of the attacks and the importance of Neighbour Discovery protocol in Internet Protocol version 6. Decision tree algorithm and Random Forest Algorithm showed high accuracy results in comparison to the other benchmarked algorithms