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Related Concept Videos

Psychology as a Science01:13

Psychology as a Science

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Psychology, as a scientific discipline, aims to understand the mind and behavior through rigorous and systematic methods. The foundation of psychological research is evidence-based, relying heavily on the scientific method to derive and validate knowledge. This structured approach ensures that findings are reliable, valid, and applicable to broader contexts.
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Biostatistics involves the application of statistical techniques to scientific research in health-related fields, including biology and public health. These techniques are essential for designing studies, collecting data, and analyzing it to draw meaningful conclusions. Given the complexity of biological processes, particularly in studies involving human subjects, biostatistical methods are crucial for effectively organizing and interpreting data that might otherwise obscure underlying patterns...
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Statistical Package for the Social Sciences (SPSS)01:22

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The Statistical Package for the Social Sciences, or SPSS, is a data management and analysis software suite. Developed by SPSS Inc. in 1968 and acquired by IBM in 2009, this tool was initially designed for social science data analysis, evolving to serve a wider range of disciplines. It was later renamed to Statistical Product and Service Solutions.
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How Data are Classified: Categorical Data01:11

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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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How Data are Classified: Numerical Data00:59

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
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Data Reporting and Recording01:24

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Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
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[Data science in psychiatry].

F E Scheepers, V Menger, K Hagoort

    Tijdschrift Voor Psychiatrie
    |March 10, 2018
    PubMed
    Summary

    Big data analytics in psychiatry offers new treatment insights by transforming patient care into a dynamic, data-driven system. This approach leverages real-time information for continuous learning and practice improvement.

    Area of Science:

    • Psychiatry
    • Data Science
    • Digital Health

    Background:

    • The rapid digitalization of society generates vast amounts of real-time data.
    • New technologies provide access to previously unavailable real-world information.
    • This influx of dynamic data presents opportunities for psychiatric treatment advancements.

    Purpose of the Study:

    • To define big data in the context of mental healthcare.
    • To explore how a big data approach can create a learning, patient-oriented healthcare system.
    • To illustrate the potential for data-driven psychiatric care.

    Main Methods:

    • A pilot study at UMC Utrecht utilizing the Cross Industry Standard Process for Interactive Data Mining (CRISP-IDM).
    • Discussion of future applications and integration of big data methodologies.

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  • Review of existing literature on data-driven insights in psychiatry.

    • Main Results:

    • The study demonstrates the feasibility of implementing big data approaches in psychiatric practice.
    • Examples show potential for rapid improvements in patient care through data analysis.
    • Existing data sources can yield new clinical insights.

    Conclusions:

    • The integration of data science into psychiatric practice opens significant new prospects.
    • A data-driven approach can enhance the efficiency and effectiveness of mental healthcare.
    • Continuous learning from data is key to advancing psychiatric treatment.