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Provider Recommendation of HPV Vaccination: Bridging the Intention-Behavior Gap
Title
Provider Recommendation of HPV Vaccination: Bridging the Intention-Behavior Gap.
Creator
Landis, Erica, Neuberger, Lindsay, Sandoval, Jennifer, Miller, Ann, University of Central Florida
Abstract / Description
The present study, guided by preproduction formative research principles, employed in-depth interviews and a brief survey with pediatric healthcare providers (N=15) to investigate the consistency between behavioral intention to strongly recommend the HPV vaccine, and implementation of the actual behavior. Specifically, the Integrative Model of Behavioral Prediction (IMBP) was used as a framework to examine the impact of skills and environmental constraints on that behavioral intention...
Show moreThe present study, guided by preproduction formative research principles, employed in-depth interviews and a brief survey with pediatric healthcare providers (N=15) to investigate the consistency between behavioral intention to strongly recommend the HPV vaccine, and implementation of the actual behavior. Specifically, the Integrative Model of Behavioral Prediction (IMBP) was used as a framework to examine the impact of skills and environmental constraints on that behavioral intention-behavioral performance relationship. Results suggest providers intend to strongly recommend the HPV vaccine at a high level, but actually recommend the vaccine with a slightly lesser frequency. A thematic analysis of interview transcripts yielded a list of skills (e.g., tact, cultural competence) and environmental constraints (e.g., a lack of policy or school entry requirement, limited time designated for each patient) that contribute to that consistency gap. Additionally, healthcare providers indicated several preferences on training design (e.g., Continuing Medical Education course, delivered by medical and communication professionals) that could be used to inform future message construction. Suggestions for overcoming the environmental constraints reported by providers are presented, and implications for incorporating the emergent skills and preferences into training as a novel strategy for improving provider communication about the HPV vaccine outlined.
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Date Issued
2016
Identifier
CFE0006132, ucf:51162
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0006132
EVOLVING MODELS FROM OBSERVED HUMAN PERFORMANCE
Title
EVOLVING MODELS FROM OBSERVED HUMAN PERFORMANCE.
Creator
Fernlund, Hans Karl Gustav, Gonzalez, Avelino J., University of Central Florida
Abstract / Description
To create a realistic environment, many simulations require simulated agents with human behavior patterns. Manually creating such agents with realistic behavior is often a tedious and time-consuming task. This dissertation describes a new approach that automatically builds human behavior models for simulated agents by observing human performance. The research described in this dissertation synergistically combines Context-Based Reasoning, a paradigm especially developed to model tactical...
Show moreTo create a realistic environment, many simulations require simulated agents with human behavior patterns. Manually creating such agents with realistic behavior is often a tedious and time-consuming task. This dissertation describes a new approach that automatically builds human behavior models for simulated agents by observing human performance. The research described in this dissertation synergistically combines Context-Based Reasoning, a paradigm especially developed to model tactical human performance within simulated agents, with Genetic Programming, a machine learning algorithm to construct the behavior knowledge in accordance to the paradigm. This synergistic combination of well-documented AI methodologies has resulted in a new algorithm that effectively and automatically builds simulated agents with human behavior. This algorithm was tested extensively with five different simulated agents created by observing the performance of five humans driving an automobile simulator. The agents show not only the ability/capability to automatically learn and generalize the behavior of the human observed, but they also capture some of the personal behavior patterns observed among the five humans. Furthermore, the agents exhibited a performance that was at least as good as agents developed manually by a knowledgeable engineer.
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Date Issued
2004
Identifier
CFE0000013, ucf:46068
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0000013
A REINFORCEMENT LEARNING TECHNIQUE FOR ENHANCING HUMAN BEHAVIOR MODELS IN A CONTEXT-BASED ARCHITECTURE
Title
A REINFORCEMENT LEARNING TECHNIQUE FOR ENHANCING HUMAN BEHAVIOR MODELS IN A CONTEXT-BASED ARCHITECTURE.
Creator
Aihe, David, Gonzalez, Avelino, University of Central Florida
Abstract / Description
A reinforcement-learning technique for enhancing human behavior models in a context-based learning architecture is presented. Prior to the introduction of this technique, human models built and developed in a Context-Based reasoning framework lacked learning capabilities. As such, their performance and quality of behavior was always limited by what the subject matter expert whose knowledge is modeled was able to articulate or demonstrate. Results from experiments performed show that subject...
Show moreA reinforcement-learning technique for enhancing human behavior models in a context-based learning architecture is presented. Prior to the introduction of this technique, human models built and developed in a Context-Based reasoning framework lacked learning capabilities. As such, their performance and quality of behavior was always limited by what the subject matter expert whose knowledge is modeled was able to articulate or demonstrate. Results from experiments performed show that subject matter experts are prone to making errors and at times they lack information on situations that are inherently necessary for the human models to behave appropriately and optimally in those situations. The benefits of the technique presented is two fold; 1) It shows how human models built in a context-based framework can be modified to correctly reflect the knowledge learnt in a simulator; and 2) It presents a way for subject matter experts to verify and validate the knowledge they share. The results obtained from this research show that behavior models built in a context-based framework can be enhanced by learning and reflecting the constraints in the environment. From the results obtained, it was shown that after the models are enhanced, the agents performed better based on the metrics evaluated. Furthermore, after learning, the agent was shown to recognize unknown situations and behave appropriately in previously unknown situations. The overall performance and quality of behavior of the agent improved significantly.
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Date Issued
2008
Identifier
CFE0002466, ucf:47715
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0002466
Towards Improving Human-Robot Interaction For Social Robots
Title
Towards Improving Human-Robot Interaction For Social Robots.
Creator
Khan, Saad, Boloni, Ladislau, Behal, Aman, Sukthankar, Gita, Garibay, Ivan, Fiore, Stephen, University of Central Florida
Abstract / Description
Autonomous robots interacting with humans in a social setting must consider the social-cultural environment when pursuing their objectives. Thus the social robot must perceive and understand the social cultural environment in order to be able to explain and predict the actions of its human interaction partners. This dissertation contributes to the emerging field of human-robot interaction for social robots in the following ways: 1. We used the social calculus technique based on culture...
Show moreAutonomous robots interacting with humans in a social setting must consider the social-cultural environment when pursuing their objectives. Thus the social robot must perceive and understand the social cultural environment in order to be able to explain and predict the actions of its human interaction partners. This dissertation contributes to the emerging field of human-robot interaction for social robots in the following ways: 1. We used the social calculus technique based on culture sanctioned social metrics (CSSMs) to quantify, analyze and predict the behavior of the robot, human soldiers and the public perception in the Market Patrol peacekeeping scenario. 2. We validated the results of the Market Patrol scenario by comparing the predicted values with the judgment of a large group of human observers cognizant of the modeled culture. 3. We modeled the movement of a socially aware mobile robot in a dense crowds, using the concept of a micro-conflict to represent the challenge of giving or not giving way to pedestrians. 4. We developed an approach for the robot behavior in micro-conflicts based on the psychological observation that human opponents will use a consistent strategy. For this, the mobile robot classifies the opponent strategy reflected by the personality and social status of the person and chooses an appropriate counter-strategy that takes into account the urgency of the robots' mission. 5. We developed an alternative approach for the resolution of micro-conflicts based on the imitation of the behavior of the human agent. This approach aims to make the behavior of an autonomous robot closely resemble that of a remotely operated one.
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Date Issued
2015
Identifier
CFE0005965, ucf:50819
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0005965
Investigating The Relationship Between Adverse Events and Infrastructure Development in an Active War Theater Using Soft Computing Techniques
Title
Investigating The Relationship Between Adverse Events and Infrastructure Development in an Active War Theater Using Soft Computing Techniques.
Creator
Cakit, Erman, Karwowski, Waldemar, Lee, Gene, Thompson, William, Mikusinski, Piotr, University of Central Florida
Abstract / Description
The military recently recognized the importance of taking sociocultural factors into consideration. Therefore, Human Social Culture Behavior (HSCB) modeling has been getting much attention in current and future operational requirements to successfully understand the effects of social and cultural factors on human behavior. There are different kinds of modeling approaches to the data that are being used in this field and so far none of them has been widely accepted. HSCB modeling needs the...
Show moreThe military recently recognized the importance of taking sociocultural factors into consideration. Therefore, Human Social Culture Behavior (HSCB) modeling has been getting much attention in current and future operational requirements to successfully understand the effects of social and cultural factors on human behavior. There are different kinds of modeling approaches to the data that are being used in this field and so far none of them has been widely accepted. HSCB modeling needs the capability to represent complex, ill-defined, and imprecise concepts, and soft computing modeling can deal with these concepts. There is currently no study on the use of any computational methodology for representing the relationship between adverse events and infrastructure development investments in an active war theater. This study investigates the relationship between adverse events and infrastructure development projects in an active war theater using soft computing techniques including fuzzy inference systems (FIS), artificial neural networks (ANNs), and adaptive neuro-fuzzy inference systems (ANFIS) that directly benefits from their accuracy in prediction applications. Fourteen developmental and economic improvement project types were selected based on allocated budget values and a number of projects at different time periods, urban and rural population density, and total adverse event numbers at previous month selected as independent variables. A total of four outputs reflecting the adverse events in terms of the number of people killed, wounded, hijacked, and total number of adverse events has been estimated. For each model, the data was grouped for training and testing as follows: years between 2004 and 2009 (for training purpose) and year 2010 (for testing). Ninety-six different models were developed and investigated for Afghanistan and the country was divided into seven regions for analysis purposes. Performance of each model was investigated and compared to all other models with the calculated mean absolute error (MAE) values and the prediction accuracy within (&)#177;1 error range (difference between actual and predicted value). Furthermore, sensitivity analysis was performed to determine the effects of input values on dependent variables and to rank the top ten input parameters in order of importance.According to the the results obtained, it was concluded that the ANNs, FIS, and ANFIS are useful modeling techniques for predicting the number of adverse events based on historical development or economic projects' data. When the model accuracy was calculated based on the MAE for each of the models, the ANN had better predictive accuracy than FIS and ANFIS models in general as demonstrated by experimental results. The percentages of prediction accuracy with values found within (&)#177;1 error range around 90%. The sensitivity analysis results show that the importance of economic development projects varies based on the regions, population density, and occurrence of adverse events in Afghanistan. For the purpose of allocating resources and development of regions, the results can be summarized by examining the relationship between adverse events and infrastructure development in an active war theater; emphasis was on predicting the occurrence of events and assessing the potential impact of regional infrastructure development efforts on reducing number of such events.
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Date Issued
2013
Identifier
CFE0004826, ucf:49757
Format
Document (PDF)
PURL
http://purl.flvc.org/ucf/fd/CFE0004826