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Application of latent class analysis in assessing the awareness, attitude, practice and satisfaction of paediatricians on sleep disorder management in children in Italy

Author

Listed:
  • Luana Nosetti
  • Maria Giovanna Paglietti
  • Luigia Brunetti
  • Luigi Masini
  • Stefania La Grutta
  • Giovanna Cilluffo
  • Giuliana Ferrante
  • Marco Zaffanello
  • Elisabetta Verrillo
  • Martino Pavone
  • Alessandra Cristina Niespolo
  • Giacomo Broggi
  • Renato Cutrera
  • on behalf of the IPRS (SIMRI)Task Force on Sleep Disordered Breathing (SDB-TF)

Abstract

Aim: To identify subgroups regarding paediatricians’ awareness, attitude, practice and satisfaction about management of Sleep-Disordered Breathing (SDB) in Italy using Latent Class Analysis (LCA). Methods: A cross-sectional study was conducted on a large sample of Italian paediatricians. Using a self-administered questionnaire, the study collected information on 420 Paediatric Hospital Paediatricians (PHPs) and 594 Family Care Paediatricians (FCPs). LCA was used to discover underlying response patterns, thus allowing identification of respondent groups with similar awareness, attitude, practice and satisfaction. A logistic regression model was used to investigate which independent variables influenced latent class membership. Analyses were performed using R 3.5.2 software. A p-value

Suggested Citation

  • Luana Nosetti & Maria Giovanna Paglietti & Luigia Brunetti & Luigi Masini & Stefania La Grutta & Giovanna Cilluffo & Giuliana Ferrante & Marco Zaffanello & Elisabetta Verrillo & Martino Pavone & Aless, 2020. "Application of latent class analysis in assessing the awareness, attitude, practice and satisfaction of paediatricians on sleep disorder management in children in Italy," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-17, February.
    • Handle: RePEc:plo:pone00:0228377
    DOI: 10.1371/journal.pone.0228377
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    References listed on IDEAS

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    1. Linzer, Drew A. & Lewis, Jeffrey B., 2011. "poLCA: An R Package for Polytomous Variable Latent Class Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i10).
    2. Ledyard Tucker & Charles Lewis, 1973. "A reliability coefficient for maximum likelihood factor analysis," Psychometrika, Springer;The Psychometric Society, vol. 38(1), pages 1-10, March.
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