فاطمه مباشری

The growing prevalence of breast implant surgery for both aesthetic and reconstructive purposes, coupled with the imperative for robust, evidence-based assessments, underscores the critical need for a standardized data collection system to capture high-quality data in the field of breast prosthetics. Consequently, the primary objective of this study was to develop a minimum data set (MDS) for a breast prosthesis registry in Iran.

This applied study was conducted in 2023. In the initial stage, a descriptive analysis was performed on medical records from Amir al-Momenin and Shahid Faqihi educational hospitals in Shiraz to identify existing breast prosthesis data elements. Subsequently, a descriptive-comparative analysis was employed to compare the required data elements in national registries of selected countries (the Netherlands, England, the United States, Korea, Sweden, and Australia). In the third stage, a Delphi questionnaire was designed and distributed to 20 plastic and cosmetic surgery specialists for validation, with 16 responding. Through two rounds of Delphi consensus, items achieving a collective agreement score above 75% were included in the final minimum data set for the breast prosthesis registry. Data analysis was performed using descriptive statistics with SPSS version 20.

Of the 79 data elements proposed for expert consensus, 53 achieved agreement. These elements were categorized into eight subsets: patient demographics (10 elements), socioeconomic information (2 elements), physician and medical characteristics (6 elements), diagnosis information and clinical history (16 elements), prosthesis characteristics (5 elements), surgical procedures (9 elements), potential complications (2 elements), and follow-up (3 elements).

It is anticipated that the proposed minimum data set will significantly contribute to information integration, enhanced quality of care, and improved safety for breast prosthesis recipients. Consequently, the inclusion of a minimum data set within the breast prosthesis registry is crucial for the collection of standardized data and the facilitation of data sharing.

Management and control of reportable diseases are challenging because these diseases include a large spectrum of infectious conditions that need accurate, precise, and timely reporting. To deal with this problem, an integrated surveillance system using the set of core data architecture principles is crucial to ensure the effective management of data. Therefore, this study aimed to identify the core data architecture requirements for effective management of Coronavirus Disease 2019 (COVID-19), followed by designing a data architecture model.

This systematic review was conducted in 2020 through searching five databases, including PubMed, Web of Science, Science Direct, and Scopus M, as well as Google scholar search engine to identify metrics for COVID-19 data architecture designing. Moreover, the search formula definition, implication of inclusion and exclusion criteria, search filtering adjustment, and related study identification were performed in this study. Subsequently, the cases identifying the architecture data of the COVID-19 component were systematically extracted and categorized in suitable classes. Finally, the management system of the architecture model of the patient was visualized in this study.

Out of 398 identified studies, 27 articles met the inclusion criteria. The obtained data were categorized into five classes, including organizations involved in data management (data producer, data users, and decision-makers), data sources, information requirements (11 information classes and 77 data elements), standards (semantic and syntactic), and control quality criteria of the data.

Implementation of customized data architecture for COVID-19 can increase the potential of the health care systems to prevent the high prevalence of this disease and improve the quality of care through timely and effective health monitoring, accurate epidemiological investigations, clinical decision supports, and health-care policymaking.

The learning environment affects the development and growth of a learner in an educational institution. Academic vitality and motivation are among the determining factors of success in the educational system. Consequently, the purpose of this study was to evaluate the academic vitality and motivation of students based on their perception of the learning environment in Abadan School of Medical Sciences, Iran.

This descriptive-analytical study was performed on the study population of all students of Abadan School of Medical Sciences. The sample size was calculated as 283 people and the samples were selected through simple random sampling method. The data were collected by the researchers using the questionnaires of learning environment evaluation and academic vitality and motivation. All the data were statistically analyzed by Pearson’s correlation test and regression test utilizing the SPSS software version 18.

Our findings demonstrated that there was a positive correlation between learning vitality and the perception of students of the learning environment. Moreover, the perceptions of students of the learning environment could be predictive of their educational vitality and academic motivation. All the components of the perception of students of the learning environment were able to predict the vitality of students. It should be noted that among the components of perception of the learning environment, only the aspect of the perception of the educational environment had a significant role in predicting academic motivation.

According to the results of the present study, a better understanding of students of the learning environment leads to higher motivation toward academic achievement in diverse educational and research fields. Furthermore, the improvement of the learning environment enhances the academic vitality of students in terms of responding to the confronted challenges and obstacles in educational environments.