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Related Concept Videos

Attribution Theory00:56

Attribution Theory

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Behavior is a product of both the situation (e.g., cultural influences, social roles, and the presence of bystanders) and of the person (e.g., personality characteristics). Subfields of psychology tend to focus on one influence or behavior over others. Situationism is the view that our behavior and actions are determined by our immediate environment and surroundings. In contrast, dispositionism holds that our behavior is determined by internal factors (Heider, 1958).
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Fundamental Attribution Error01:14

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According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
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Attribution01:26

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In social interactions, individuals frequently seek to understand the motivations and causes behind others' behaviors. This fundamental aspect of social perception, known as attribution, plays a crucial role in shaping interpersonal relationships and guiding future actions. Attribution refers to the cognitive process through which people infer the reasons behind others' behaviors, allowing them to assess character traits, intentions, and situational influences.Attribution Theory and Its...
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GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

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A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
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Personal Choice and Fate Attributions01:19

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Some individuals interpret life events as a consequence of their personal choices and actions, while others believe that outcomes are dictated by fate or destiny. This divergence in perspective has been examined in psychological and cross-cultural studies, particularly in relation to religious faith and cultural beliefs about causality.Fate and Personal ResponsibilityPeople who emphasize personal responsibility view events as direct consequences of their decisions. For instance, breaking a leg...
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Model Approaches for Pharmacokinetic Data: Physiological Models01:15

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Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
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Chronic Salmonella Infected Mouse Model
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A Modular Bayesian Salmonella Source Attribution Model for Sparse Data.

Antti Mikkelä1, Jukka Ranta1, Pirkko Tuominen1

  • 1Risk Assessment Unit, Finnish Food Authority, Helsinki, Finland.

Risk Analysis : an Official Publication of the Society for Risk Analysis
|March 21, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a robust Bayesian salmonella source attribution model, adaptable for sparse data. The model effectively allocates human salmonellosis cases to their food sources using multi-year data.

Keywords:
Modular Bayesian modelsalmonellasource attributionsparse data

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Area of Science:

  • Foodborne illness epidemiology
  • Statistical modeling
  • Microbial source tracking

Background:

  • Salmonella source attribution is crucial for public health interventions.
  • Existing statistical models often lack generalizability due to data sensitivity.

Purpose of the Study:

  • To develop a generally applicable Bayesian salmonella source attribution model.
  • To address limitations of existing models with small, sparse, multi-year datasets.

Main Methods:

  • A modularized Bayesian approach integrating exposure, subtype distribution, and epidemiological models.
  • Utilized multi-year consumption data, Salmonella prevalence, and bacterial typing results.
  • Incorporated source-specific and subtype-specific effects for enhanced accuracy.

Main Results:

  • The model successfully allocated human salmonellosis cases to eight food sources.
  • Demonstrated applicability to sparse, multi-year Salmonella data.
  • Provided smoothed estimations of annual Salmonella subtype proportions.

Conclusions:

  • The developed Bayesian model offers a robust framework for Salmonella source attribution.
  • The modular approach enhances parameter estimation and model applicability.
  • This method aids in identifying key sources of human Salmonella infections.