INTRODUCTION: The application of the best approaches to teach adults in medical education is important in the process of training learners to become and remain effective health care providers. This research aims at designing and integrating two approaches, namely team teaching and case study and tries to examine the consequences of these approaches on learning, self regulation and self direction of nursing students. MATERIAL & METHODS: This is aquasi experimental study of 40 students who were taking a course on mental health. The lessons were designed by using two educational techniques: short case based study and team based learning. Data gathering was based on two valid and reliablequestionnaires: Self-Directed Readiness Scale (SDLRS) and the self-regulating questionnaire. Open ended questions were also designed for the evaluation of students'with points of view on educational methods. RESULTS: The Results showed an increase in thestudents'self directed learning based on their performance on the post-test. The results showed that the students'self-directed learning increased after the intervention. The mean difference before and after intervention self management was statistically significant (p=0.0001). Also, self-regulated learning increased with the mean difference after intervention (p=0.001). Other results suggested that case based team learning can have significant effects on increasing students’ learning (p=0.003). CONCLUSION: This article may be of value to medical educators who wish to replace traditional learning with informal learning (student-centered-active learning), so as to enhance not only the students' 'knowledge, but also the advancement of long- life learning skills.

Background: Hospitals are one of the most important health facilities in every society. Therefore, these facilities should be located in a rational manner. The present study aims to 1) select optimal locations for establishing new hospitals in Shiraz and 2) assess the location of the existing hospitals in Shiraz using the geographical information system (GIS). Methodology: At first, the hospital location allocation criteria were identified through reviewing the literature. Then, the criteria were entered into a questionnaire, which was completed by experts based on analytic hierarchy process (AHP). Afterwards, spatial raster layers of all the criteria were created in the Arc GIS 10.3 software. Then, in order to select optimal locations for hospitals, weights of the criteria were calculated based on AHP in Arc GIS and the weights were exerted on the relevant spatial created layers. Combination of the layers was presented on a map. After evaluating the 33 existing hospitals’ locations based on the map, the areas located in standard hospital service areas (1500 m) were erased from the map in order to exclude the served areas. Results: The results revealed that ‘proximity to the main roads’ was the most important criterion amongst the whole applied criteria for selecting hospital location. This study also presented 15 land parcels, located in northern part of the city, as the best lands for constructing new hospitals. Besides, according the applied criteria in this study, none of the 33 existing hospitals were located in the appropriate locations. Conclusions: Considering the obtained pattern of the existing hospitals’ locations, in most of the cases, the existing location is not good and the future hospitals must be located in more appropriate locations. The results of this study can be useful for health policymakers to select the most suitable locations for constructing new hospitals in future. Keywords: Geographical Information System; Hospital; Health; Location-Allocation; Site selection; Spatial-AHP

Background: Despite the importance of the privacy and confidentiality of patients’ information, mobile health (mHealth) apps can raise the risk of violating users’ privacy and confidentiality. Research has shown that many apps provide an insecure infrastructure and that security is not a priority for developers. Objective: This study aims to develop and validate a comprehensive tool to be considered by developers for assessing the security and privacy of mHealth apps. Methods: A literature search was performed to identify papers on app development, and those papers reporting criteria for the security and privacy of mHealth were assessed. The criteria were extracted using content analysis and presented to experts. An expert panel was held for determining the categories and subcategories of the criteria according to meaning, repetition, and overlap; impact scores were also measured. Quantitative and qualitative methods were used for validating the criteria. The validity and reliability of the instrument were calculated to present an assessment instrument. Results: The search strategy identified 8190 papers, of which 33 (0.4%) were deemed eligible. A total of 218 criteria were extracted based on the literature search; of these, 119 (54.6%) criteria were removed as duplicates and 10 (4.6%) were deemed irrelevant to the security or privacy of mHealth apps. The remaining 89 (40.8%) criteria were presented to the expert panel. After calculating impact scores, the content validity ratio (CVR), and the content validity index (CVI), 63 (70.8%) criteria were confirmed. The mean CVR and CVI of the instrument were 0.72 and 0.86, respectively. The criteria were grouped into 8 categories: authentication and authorization, access management, security, data storage, integrity, encryption and decryption, privacy, and privacy policy content. Conclusions: The proposed comprehensive criteria can be used as a guide for app designers, developers, and even researchers. The criteria and the countermeasures presented in this study can be considered to improve the privacy and security of mHealth apps before releasing the apps into the market. Regulators are recommended to consider an established standard using such criteria for the accreditation process, since the available self-certification of developers is not reliable enough.

Introduction: Mental health problems as a consequence of cancer lower the quality of life of cancer patients. Despite increasing studies of breast cancer‐focused mobile health applications (m‐Health apps), there is less research on breast cancer patients' quality of life or well‐being. The purpose of this study is to develop and evaluate the usability and quality of an educational m‐Health app aimed at improving the resilience of breast cancer in women.Methods: This study was conducted in four phases. It included extracting the requirements of the app through the nominal group technique. Based on these results, an m‐Health app was developed and evaluated in terms of usability and quality by two scales, System Usability Scale and Mobile App Rating Scale questionnaires, respectively. Finally, the role of patients' age and educational backgrounds in the use of the app was assessed. The relationship between learnability and usability of the app was measured by the T‐Test.Results: The app was developed with three user interfaces. Its usability developed from the patient's point of view scored a remarkable score of 83.20 with a 95% confidence interval. This value was too indicative of high satisfaction with the usefulness and the possibility of recommending it to other cancer survivors. The results of the quality evaluation from an expert's point of view showed that this app had good functionality. Evaluation of the role of demographic information in the use of the app showed that it can be used for all age groups with different levels of education. The app did not differ significantly between learnability and usability.Conclusion: The development of m‐Health apps, based on usability principles that are suitable for all age groups with different levels of education, is welcomed by cancer patients.

Background: During disasters or crises, the traditional models of supply chain encounter failure and skewedness under the inevitable and unknown pressures. The procurement and transformation of required equipment to the involved areas is considered as one of the main triggers of decreasing damages and losses during crisis. In this regard, a breakdown in pharmaceutical supply chain can lead to intensive, undesired consequences. Methods: This was a qualitative study applying a grounded theory approach. The study was conducted with attending of 32 informant participants who were qualified in supply chain during natural disasters and crisis. In order to collect the data, deep semi-structured interviews were applied along with investigating the documents, observation, field notes and theoretical memos. For data analysis, a continuous comparison was used according to Corbin and Strauss method. Results: Results of the study were categorized in 8 main categories as the main themes. “Wasting” appeared as the main factor of the resilience of pharmaceutical and consumable medical equipment supply chain. Wasting included two subthemes of loss of resources and wasting time. Conclusion: In order to make resilience in pharmaceutical and consumable medical equipment during disasters, it is necessary to reinforce the various dimensions of the resilience model to increase the rate of supply chain responsiveness. This study particularly contributes to broadening and deepening our understanding of how to mitigate the risk of undesirable outcomes of pharmaceutical supply chain during the disasters or crises.

Obstructive Sleep Apnea (OSA) is the most common breathing-related sleep disorder, leading to increased risk of health problems. In this study, we investigated and evaluated the supervised machine learning methods to predict OSA. We used popular machine learning algorithms to develop the prediction models, using a dataset with non-invasive features containing 231 records. Based on the methodology, the CRISP-DM, the dataset was checked and the blanked data were replaced with average/most frequented items. Then, the popular machine learning algorithms were applied for modeling and the 10-fold cross-validation method was used for performance comparison purposes. The dataset has 231 records, of which 152 (65.8%) were diagnosed with OSA. The majority was male (143, 61.9%). The results showed that the best prediction model with an overall AUC reached the Naïve Bayes and Logistic Regression classifier with 0.768 and 0.761, respectively. The SVM with 93.42% sensitivity and the Naïve Bayes of 59.49% specificity can be suitable for screening high-risk people with OSA. The machine learning methods with easily available features had adequate power of discrimination, and physicians can screen high-risk OSA as a supplementary tool.