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Exploring the role of socioeconomic status and psychological characteristics on talent development in an English soccer academy.

Adam L KellyCraig A WilliamsDaniel T JacksonJennifer TurnnidgeMatthew J ReevesJames H DugdaleMark R Wilson
Published in: Science & medicine in football (2023)
Social factors and psychological characteristics can influence participation and development in talent pathways. However, the interaction between these two factors is relatively unknown. The aim of this study was to investigate the implications of socioeconomic status and psychological characteristics in English academy soccer players ( n =58; aged 11 to 16 years). To assess socioeconomic status, participants' home postcodes were coded according to each individual's social classification and credit rating, applying the UK General Registrar Classification system and Cameo TM geodemographic database, respectively. Participants also completed the six factor Psychological Characteristics for Developing Excellence Questionnaire (PCDEQ). A classification of 'higher-potentials' ( n =19) and 'lower-potentials' ( n =19) were applied through coach potential rankings. Data were standardised using z -scores to eliminate age bias and data were analysed using independent sample t -tests. Results showed that higher-potentials derived from families with significantly lower social classifications ( p =0.014) and reported higher levels for PCDEQ Factor 3 (coping with performance and developmental pressures) ( p =0.007) compared to lower-potentials. This study can be used to support the impetus for researchers and practitioners to consider the role of social factors and psychological characteristics when developing sporting talent. For example, facilitating player-centred development within an academy and, where necessary, providing individuals with additional support.
Keyphrases
  • healthcare
  • mental health
  • machine learning
  • deep learning
  • emergency department
  • primary care
  • big data
  • depressive symptoms
  • sleep quality
  • climate change
  • data analysis