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Identity, cognitive structure, and long-term tranquillizer use: a multidimensional approach.

S Larsson1, J Lilja, D Hamilton

  • 1Karolinska Institute, Centre for Dependency Disorders, Karolinska Hospital, Stockholm, Sweden. sam.larsson@socarb.su.se

Substance Use & Misuse
|October 11, 2001
PubMed
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This study explores various theories for analyzing long-term tranquilizer use, proposing a multidimensional model linking identity structure and use. It advocates for mixed methods, including quantitative and qualitative approaches, for comprehensive analysis.

Area of Science:

  • Psychology
  • Sociology
  • Anthropology

Background:

  • Long-term tranquilizer use presents complex analytical challenges.
  • Existing theoretical frameworks offer partial insights into this phenomenon.

Purpose of the Study:

  • To review diverse theoretical perspectives relevant to long-term tranquilizer use.
  • To introduce a multidimensional model connecting identity structure and tranquilizer use.
  • To advocate for integrated methodological strategies in analyzing tranquilizer use.

Main Methods:

  • Review of behavioral, cognitive, humanistic, existential, transpersonal, psychodynamic, sociological, and anthropological theories.
  • Presentation of a multidimensional model.
  • Discussion of mixed methodological strategies (quantitative and qualitative).

Related Experiment Videos

  • Analysis of the actor-spectator paradox in data interpretation.

    • Main Results:

    • A comprehensive overview of relevant theoretical perspectives is provided.
    • A novel multidimensional model is proposed to understand the link between identity and long-term tranquilizer use.
    • The utility of mixed methods for a nuanced analysis is highlighted.

    Conclusions:

    • A multidimensional approach integrating various theoretical perspectives is crucial for understanding long-term tranquilizer use.
    • Mixed methodological strategies enhance the depth and validity of research findings.
    • Consideration of the actor-spectator paradox is essential for accurate data analysis.