Showing 6 results for Intelligence
Manoochehr Mahram, Farnaz Karimi,
Volume 16, Issue 7 (10-2013)
Abstract
Background: Human brain consists of two hemispheres with contralateral control of the body. One hemisphere's function is predominate to another, so one side of the body is more powerful in performing special tasks comparing the other which this property is almost used to determine predominant hemisphere of the brain. This analytic study performed to assess the effect of handedness and gender on the result of school readiness assessment examination in children.
Materials and Methods: Of 6 to 6.5 years old children living in middle socioeconomic regions of Qazvin city, referred to preschool Examining center to assess educational readiness and other physical examination, 400 children equally of both sexes were randomly selected in two Left-Handed (LH) and Right-Handed (RH) groups to compare the results of school readiness assessment examination. The data was statistically analysed and P-Value less than 0.05 was considered significant.
Results: The results of School Readiness Assessment Examination in LH and RH children were 38.71±2.70 and 38.15±4.04, respectively without significant difference. These results were 38.8±3.8 and 38.30±3.4 in boys and girls respectively, without significant difference. Comparing RH and LH cases in each sex group did not show any significance.
Conclusion: No significance was observed comparing the results of School Readiness Assessment Examination in LH and RH groups and between two gender groups.
Rahmatallah Jadidi, Fereshteh Memari, Zohreh Anbari ,
Volume 16, Issue 8 (11-2013)
Abstract
Background: According to the organizational intelligence to increase access of the knowledge in organizations and can to create competitive advantage in order to improve efficiency & effectiveness in organization, but that are affected by the structural dimensions of centralization, formalization, complexity (which reflects the characteristics of the internal organizations). This study aimed to investigate the relationship between organizational structure & organizational intelligence in Arak Medical University affiliated teaching hospital was performed.
Materials and Methods: In this study, the research community was comprised official staff that have high school diploma, working in hospitals that were selected for this study by sample stratified random studied. Tool for data collection, was questionnaire Robbins organizational structure and standardized questionnaire Alberkht about organizational intelligence. After confirming the validity and reliability of questionnaires, these were distributed between colleagues in teaching hospitals and then were collected. Using data collected from 16 SPSS software testing was analyzed by Spearman correlation coefficient.
Results: From 87 respondents, most of them have had a bachelor's degree and about 52 people who (74%) had experience about five years. The results were shown of the significant correlation between the organizational structure &intelligence in teaching hospital (r=-0.612 and p=0.001). The relationship between the complexity of organizational with organizational intelligence was not significant (r=0.157 and p=0.53), but by two other organizational dimension of structures (i.e., degree of formalization, centralization) with organizational intelligence was obtained significant respectively (r=-0.693 and p=0.001) and (r=-0.711 and p=0.001).
Conclusion: Based on findings from this research, teaching hospitals must be review current situation about organizational structural dimensions particular through decreasing centralization, formalization, to provide necessary field for developing and implementation of organizational intelligence
Sholeh Zakiani , Saied Ghaffari ,
Volume 22, Issue 3 (8-2019)
Abstract
Background and Aim: Promoting spiritual intelligence and adherence to ethics leads to higher quality service, efficiency and effectiveness. The present study was conducted to investigate the relationship between the spiritual intelligence of librarians and the quality of services in the libraries of Shahid Beheshti University of Medical Sciences with a professional ethics approach.
Materials and Methods: The research method was descriptive-correlational and with an objective purpose. The statistical population included 180 librarians working in the library of Shahid Beheshti University of Medical Sciences. Data collection was done by two questionnaires of King and Radad. Data analysis was done by inferential methods and Kolmogrov-Smirnov test. Data were analyzed by SPSS version 22 software.
Ethical Considerations: In this study, all principles of research ethics were considered.
Findings: The results showed that there is a positive and significant relationship between the dimensions of spiritual intelligence(critical existential thinking, production of personal meaning, transcendental consciousness, and extension of consciousness) and the quality of services in the libraries.
Conclusion: The result of the research showed that there is a relationship between the spiritual intelligence of librarians and the provision of quality services in the libraries of Shahid Beheshti University of Medical Sciences with the professional ethics approach. Therefore, using the spiritual intelligence, service quality in the studied libraries could be increased.
Marzieh Ganjavi, Alireza Manzari Tavakoli, Zahra Zeinaddiny Meimand,
Volume 27, Issue 3 (7-2024)
Abstract
Introduction: Delinquency is a serious challenge for teenagers and has significant negative social effects. The main goal of this research was to find out the structural equation modeling of extraversion and delinquent behavior disorder: the mediating role of moral intelligence among the juveniles of Kerman Correctional Center.
Methods: This was a descriptive correlational research of structural equation model type. The statistical population of this research was made up of 80 juveniles of Kerman Correctional Center, who were selected and studied by simple random sampling using Morgan's table. To collect information, Hans Eysenck's (1963) Personality Type Questionnaire, Goodman's Conduct Disorder Questionnaire (1997), Aiti Juvenile Delinquency Questionnaire (2013) and Link and Keel's Moral Intelligence Questionnaire (2005) were used. Descriptive and inferential statistics (structural equation modeling) were used for data analysis through SPSS-28 and Smart PLS-3 software.
Results: The results of this research showed that there is a direct and positive relationship between extraversion and juvenile delinquency. There is a significant direct and positive relationship between conduct disorder and juvenile delinquency. There is a direct and positive relationship between extroversion and moral intelligence of teenagers. There is a significant direct and negative relationship between conduct disorder and moral intelligence of adolescents. There is a direct negative relationship between moral intelligence and delinquency. There was no relationship between extraversion and juvenile delinquency as a mediator of moral intelligence. There was no relationship between conduct disorder and juvenile delinquency with the mediating role of moral intelligence.
Conclusions: According to the results, it can be acknowledged that moral intelligence is an effective component of delinquency affected by extroversion and behavior disorder in teenagers. Therefore, education and training programs should be implemented to strengthen moral intelligence in schools and families, because these programs can strengthen moral skills and moral decision-making power in teenagers and help reduce behavioral disorders and, as a result, delinquency.
Seyed Sadegh Hosseini, Mohammad Reza Yamaghani,
Volume 27, Issue 4 (10-2024)
Abstract
Introduction: Nowadays, the use of artificial intelligence and machine learning has impacted all fields of study. Utilizing these methods for identifying individuals' emotions through integrating audio, text, and image data has shown higher accuracy than conventional methods, presenting various applications for psychologists and human-machine interaction. Identifying human emotions and individuals' reactions is crucial in psychology and psychotherapy. Emotional identification has traditionally been conducted individually and by analyzing facial expressions, speech patterns, or handwritten responses to stimuli and events. However, depending on the subject's conditions or the analyst's circumstances, this approach may lack the required accuracy. This paper aimed to achieve high-precision emotional recognition from audio, text, and image data using artificial intelligence and machine learning methods.
Methods: This research employs a correlation-based approach between emotions and input data, utilizing machine learning methods and regression analysis to predict a criterion variable based on multiple predictor variables (the emotional category as the criterion variable and the features, audio, image, and text variables as predictors). The statistical population of this study is the IEMOCAP dataset, and the data type of this research is a mixed quantitative-qualitative approach.
Results: The results indicated that combining audio, image, and text data for multi-modal emotional recognition significantly outperformed the recognition of emotions from each data alone, exhibiting a precision of 82.9% in the baseline dataset.
Conclusions: The results demonstrate a considerably acceptable precision in identifying human emotions through audio integration, text, and image data compared to individual data when using machine learning and artificial intelligence methods.
Miramirhossein Seyednazari, Hamed Gholizad Gougjehyaran, Amin Sohaili, Amirmohammad Drosti, Rasul Asghari,
Volume 28, Issue 5 (12-2025)
Abstract
The rapid integration of Artificial Intelligence (AI) into medical sciences, while promising transformative breakthroughs in early diagnosis and personalized treatments (1), introduces a profound ethical and legal challenge: the management of the vast, sensitive, and unprecedented volume of health data and the preservation of patient privacy. The nature of this data, which includes clinical records, radiological images, genetic data, and even data from wearable health devices 2, extends beyond traditional identifiable information. It possesses the capability to reconstruct a comprehensive profile of an individual, rendering complete and permanent de-identification virtually impossible (1).
This massive volume of information has become the main fuel for deep learning algorithms, but any breach or disclosure could lead to serious discrimination in access to insurance, employment, and even judicial decision-making (2). The lack of Transparency regarding how these data are processed and analyzed by the algorithms, which often function as a "black box," erodes the trust of both patients and physicians (3). Healthcare providers cannot understand the AI's decision-making process, which not only hinders clinical adoption but also creates a legal gray area concerning accountability in the event of diagnostic or therapeutic error (4).
The current legal challenge stems from the fact that existing privacy laws were not designed to address advanced algorithms and real-time data collection (1, 4). AI constantly outpaces existing legal frameworks by creating novel methods of knowledge extraction from raw data. Furthermore, due to their reliance on large data networks, AI tools are exposed to advanced cyberattacks, which could lead to the mass disclosure of confidential data (5). Consequently, in the absence of a robust and up-to-date data governance framework, AI's potential to improve public health is accompanied by the risk of undermining human dignity and violating fundamental patient rights (6).
To ensure that AI innovations advance with ethical and legal compliance, urgent measures must be taken to establish a comprehensive regulatory framework. This requires formulating a new, dynamic model of informed consent that goes beyond a one-time agreement, allowing patients continuous and informed control over how their data is used at different stages of AI training and deployment. Concurrently, developers must be mandated to embed privacy protection at the core design of every AI tool, which means utilizing advanced privacy-preserving techniques such as differential privacy and federated learning for on-premise data processing. Additionally, a multi-disciplinary oversight body composed of ethics, legal, computer science, and clinical experts must be established, ensuring that every AI tool undergoes a rigorous and transparent ethical and technical assessment and approval process before entering the clinical environment, thereby preventing potential biases and algorithmic errors. These measures will not only protect patients against misuse but also provide the necessary trust for the sustainable and safe advancement of this vital technology in society
