<?xml version="1.0" encoding="UTF-8"?>
<ArticleSet>
  <Article>
    <Journal>
      <PublisherName></PublisherName>
      <JournalTitle>Journal of Foresight and Health Governance</JournalTitle>
      <Issn>3092-6173</Issn>
      <Volume>2</Volume>
      <Issue>Serial Number 5</Issue>
      <PubDate PubStatus="epublish">
        <Year>2025</Year>
        <Month>08</Month>
        <Day>01</Day>
      </PubDate>
    </Journal>
    <ArticleTitle>Future Directions in Talent Identification for Track and Field: Integrating Science and Multidisciplinary Perspectives</ArticleTitle>
    <VernacularTitle>Future Directions in Talent Identification for Track and Field: Integrating Science and Multidisciplinary Perspectives</VernacularTitle>
    <FirstPage>1</FirstPage>
    <LastPage>12</LastPage>
    <Language>EN</Language>
    <AuthorList>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
      <Author>
        <FirstName></FirstName>
        <LastName></LastName>
        <Affiliation></Affiliation>
      </Author>
    </AuthorList>
    <PublicationType>Journal Article</PublicationType>
    <History>
      <PubDate PubStatus="received">
        <Year>2025</Year>
        <Month>03</Month>
        <Day>08</Day>
      </PubDate>
    </History>
    <Abstract>&lt;table&gt;
&lt;tbody&gt;
&lt;tr&gt;
&lt;td&gt;
&lt;p&gt;The identification of talent in athletics remains an essential but delicate problem with the sport's physiological, biomechanical, technical, and psychological heterogeneity. Traditional approaches have relied primarily on results from performance and mere anthropometrics, being prone to bringing forward or biased selections dependent on relative age effects and imbalance in training access. This narrative synthesis combines the current evidence and considers the directions for more efficient, equitable, and evidence-based sporting ability detection systems. It outlines the limitations of uni-dimensional models and proposes multidisciplinary syntheses, involving physiological profiling, biomechanical analysis, psychological testing, and longitudinal observation. New and emerging technologies like wearable sensors, artificial intelligence, machine learning, and digital data analytics are debated for their potential to enhance the accuracy and objectivity of sporting ability detection. The review stresses the need for standardized, culturally sensitive, and ethically sound models that adhere to the principles of long-term athlete development rather than short-term performance. By integrating evidence from sport science, technology, and social environments, future talent identification systems will be more holistic, valid, and inclusive, leading to an improved efficiency of athlete development models in track and field.&lt;/p&gt;
&lt;/td&gt;
&lt;/tr&gt;
&lt;/tbody&gt;
&lt;/table&gt;</Abstract>
    <ObjectList>
      <Object Type="keyword">
        <Param Name="value">Talent Identification</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Track and Field</Param>
      </Object>
      <Object Type="keyword">
        <Param Name="value">Future Directions</Param>
      </Object>
    </ObjectList>
    <ArchiveCopySource DocType="pdf">https://www.journalfhg.com/index.php/jfph/article/download/29/26</ArchiveCopySource>
  </Article>
</ArticleSet>
