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The Role Of Prior Knowledge On Entrepreneurial Alertness Level And Opportunity Recognition In The Field Of Health Entrepreneurship
Elahe Meigounpoory , Mohammadreza Meigounpoory, Ehtesham Seidali Roote ,
Volume 8, Issue 3 (9-2014)
Abstract

 Background and Aim: Nowadays, entrepreneurs in different fields of health, have an important role in economic development. Prior knowledge has an effect on entrepreneurial alertness and opportunity recognition and with considering the lack of research in this important field, the aim of this study is to evaluate the effect of various aspects of prior knowledge on entrepreneurial alertness & and opportunity recognition .

 Materials and Methods: To implement this descriptive-survey study, a questionnaire based on Likert scale sent to 63 active health field managers in east of Tehran, where 52 people responded to the questions. In this study the effect of eight variables of prior knowledge were investigated on entrepreneurial alertness and opportunity recognition. Data were analyzed using SPSS 15 and Spearman correlation test.

 Results: Results showed that majori ty of perior knowledge aspects have meaningful relationship to entrepreneurial alertness and opportunity recognition . However, relationship between component of technology skills with both variables was not confirmed. Also, relationship between education level and entrepreneurial alertness, there was no.

 Conclusion: Reinforcing of prior knowledge component led to increased entrepreneurship.


Expected Competencies and Capabilities of Data Librarians in Research Data Management According to the Harvard Biomedical Data Life Cycle: A Scoping Review
Zohreh Ehteshami, Azam Shahbodaghi, Mohammad Javad Mansourzadeh,
Volume 18, Issue 5 (11-2024)
Abstract
Background and Aim: An efficient data librarian equipped with the necessary competencies and capabilities is one of the most crucial elements in managing research data. The aim of this study is to identify the expected competencies and capabilities for data librarians in research data management according Harvard Biomedical Data Life Cycle.
Materials and Methods: This study is a scoping review, utilizing the Harvard Biomedical Data Lifecycle model to systematically present the findings. To retrieve relevant literature, a search strategy was employed using related keywords in databases such as Scopus, PubMed, Web of Science, Google scholar and other reputable domestic databases, over the past five years. The research population comprised original research articles published in Persian and English that addressed the expected skills and capabilities for data librarians in managing research data.
Results: Out of 5064 documents found, 196 were selected for full-text review. After reviewing the full texts, 17 studies were included in the research. In total, 92 competencies and capabilities were identified across 23 processes within the 7 stages of the Harvard Biomedical Data Lifecycle: 16 in the first stage, 16 in the second stage, 7 in the third stage, 15 in the fourth and fifth stages, 12 in the sixth stage, 8 in the seventh stage, and 18 general competencies and capabilities. According to the findings, the most studies focused on the competencies and capabilities required for the second stage, “Collection and Creation,” while the fewest studies addressed the seventh stage, “Publish and Reuse.” No studies mentioned competencies and capabilities for the processes “Image Management” in the third stage and “Preprints and Publishing” in the seventh stage.
Conclusion: The results of this study indicate that among the various stages of the data lifecycle, the “Collection and Creation” stage received the most attention. Additionally, data librarians should possess not only specialized and professional skills but also general competencies and capabilities. It is recommended that the findings of this research be considered for designing short-term and long-term educational programs to train data librarians for research data managenet.

Case-Based Reasoning: An Applied Technique for Designing Intelligent System for the Differential Diagnosis of Oral Diseases
Reza Safdari, Arash Mansourian, Shahram Tahmasebian, Niloofar Mohammadzadeh, Hamideh Ehtesham,
Volume 19, Issue 6 (3-2026)
Abstract
Background and Aim: Artificial intelligence-based systems can facilitate data management and interpretation in various dental specialties and can be used as auxiliary tools in diagnosis and education. Case-based reasoning is a promising artificial intelligence method for implementing decision support systems in medical sciences. In the current research, this technique has been used to design an intelligent system for the differential diagnosis of oral diseases.
Materials and Methods: This research is a developmental study and is applied in terms of results. To create a database of cases, patient data was collected by referring to the specialized polyclinic of the Faculty of Dentistry at Tehran University of Medical Sciences and through clinical interviews. The [feature-value] collection was used to display the cases. The weight of the features was determined through a specialized Delphi survey conducted at the national level and as an online study. The determined weights were stored in the case database and used as similarity evaluation parameters. Then, the similarity index was calculated for each case.
Results: The intelligent system designed in this research has been developed based on web technologies. Problem-solving in the case-based reasoning method is done in a cycle and includes four main stages: recovery, reuse, review, and maintenance. The input parameters of the system include clinical indicators, paraclinical indicators, historical data, and management data affecting the diagnosis process. The system provides a prioritized list of differential diagnoses of oral diseases across six main axes as output including Ulcerative, vesicular, and bullous lesions, Red and white lesions of the oral mucosa, Pigmented lesions of the oral mucosa, Benign lesions of the oral cavity, Oral cancer, Salivary gland diseases.
Conclusion: The development of the system utilizing case-based reasoning techniques and clinical data processing has the potential to assist dentists in achieving differential diagnosis across six main areas of oral diseases.

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