DEVELOPMENT AND VALIDATION OF THE “EXPRESS TECHNIQUE FOR ASSESSING CURRENT RECOVERY RESOURCES AND BARRIERS”

Cover Page

Cite item

Full Text

Abstract

The article presents the results of the initial psychometric validation of the modular “Express Technique for Assessing Current Recovery Resources and Barriers”, developed to diagnose the recovery potential of individuals with addictive behaviour during the post-rehabilitation period. The study involved 361 people with gambling addiction who had completed a rehabilitation programme (remission group N = 183; relapse group N = 178). Psychometric analysis confirmed internal (α, ω = 0.83–0.91; MIIC = 0.45–0.56), structural (CFA: CFI = 0.918; TLI = 0.905; RMSEA = 0.067), discriminant (p < 0.005; r = 0.57–0.61), and convergent validity (ρ = 0.50–0.81). The results indicate the instrument’s reliability and theoretical consistency at the stage of primary validation and demonstrate its potential for further psychometric refinement and practical application in assessing the dynamics of recovery resources among individuals with various types of addictions.

About the authors

Anna Yu. Zinina

Saint Petersburg State University

ORCID iD: 0000-0001-9391-102X

Svetlana Dz. Gurieva

Saint Petersburg State University

ORCID iD: 0000-0002-4305-432X

References

  1. Ghelfi M. et al. Online gambling: A systematic review of risk and protective factors in the adult population //Journal of gambling studies. 2024. Vol. 40. No. 2. P. 673–699.
  2. Overå S. et al. Risks and Harms associated with Online Gaming and Gambling Report. 2024. 46 p.
  3. World Health Organization. International Classification of Diseases, 11th Revision (ICD-11): Gaming disorder [Electronic resource]. Geneva: World Health Organization, 2021. URL: https://www.who.int/standards/classifications/frequently-asked-questions/gaming-disorder (дата обращения: 14.10.2025).
  4. Goodwin B. C. et al. A typical problem gambler affects six others // International gambling studies. 2017. Vol. 17. No. 2. P. 276–289.
  5. Chóliz M. Is gambling like a virus?: A conceptual framework and proposals based on empirical data for the prevention of gambling addiction //BMC Public Health. 2023. Vol. 23. No. 1. P. 1686. https://doi.org/10.1186/s12889-023-16610-x
  6. Abbott M. W. The changing epidemiology of gambling disorder and gambling-related harm: public health implications // Public health. 2020. Vol. 184. P. 41–45. https://doi.org/10.1016/j.puhe.2020.04.003
  7. Zinina A. Y., Gurieva S. D. Transformation of relationships between time perspective and lifesense orientations in people with gambling disorder in a rehabilitation setting //Journal of Gambling Issues. 2024. Vol. 54. P. 49–66.
  8. Cloud W., Granfield R. Conceptualizing recovery capital: Expansion of a theoretical construct //Substance use & misuse. 2008. Vol. 43. No. 12–13. P. 1971–1986. https://doi.org/10.1080/10826080802289762.
  9. Hennessy E. A. Recovery capital: A systematic review of the literature // Addiction Research & Theory. 2017. Vol. 25. No. 5. P. 349–360.
  10. Gavriel-Fried B., Lev-El N., Kraus S. W. The holistic recovery capital in gambling disorder index: A pilot study // Journal of Behavioral Addictions. 2022. Vol.11. No. 2. P. 600–606.
  11. Ядов В. А. О диспозиционной регуляции социального поведения личности // Методологические проблемы социальной психологии. 1975. 89 с.
  12. Шамионов Р., Бескова Т. Методика диагностики субъективного благополучия личности // Психологические исследования. 2018. Т. 11. No. 60. 12 с.
  13. Fleiss J. L. Measuring nominal scale agreement among many raters // Psychological bulletin. 1971. Vol. 76. No. 5. 378 p.
  14. DeVellis R. F., Thorpe C. T. Scale development: Theory and applications. – Sage publications. 2021. 321 p.
  15. Kline P. A. A Handbook of Test Construction: Introduction to Psychometric Design. 2016. 260 p.
  16. Clark L. A., Watson D. Constructing validity: Basic issues in objective scale development. 2016. PP. 187–203.
  17. Kline R. B. Principles and practice of structural equation modeling. – Guilford publications. 2023. 494 p.
  18. Brown T. A. Confirmatory factor analysis for applied research. –Guilford publications. 2015. 462 p.
  19. Field A. Discovering statistics using IBM SPSS statistics. – Sage publications limited. 2024. 1144 p.
  20. Cohen J. Statistical power analysis for the behavioral sciences. –routledge, 2013. 567 p.
  21. Nunnally J. C., Bernstein I. H. Psychometric theory 3rd ed., New York. 1994. 752 p.
  22. Hobfoll S. E. Social and psychological resources and adaptation //Review of general psychology. 2002. Vol. 6. No. 4. P. 307–324.
  23. Rosenthal R. et al. Parametric measures of effect size //The handbook of research synthesis. 1994. Vol.621. No. 2. P. 231–244.
  24. Best D. et al. Bridging the gap: Building and sustaining recovery capital in the transition from prison to recovery residences // Journal of offender rehabilitation. 2024. Vol. 63. No.1. P. 21–36. https://doi.org/10.1080/10509674.2023.2286648
  25. Day E. et al. Recovery support services as part of the continuum of care for alcohol or drug use disorders //Addiction. 2025. P. 1497-1520.
  26. Stellern J. et al. Emotion regulation in substance use disorders: A systematic review and meta‐analysis //Addiction. 2023. Vol. 118. No. 1. P. 30–47.
  27. González-Roz A. et al. Emotional dysregulation in relation to substance use and behavioral addictions: Findings from five separate meta-analyses // International Journal of Clinical and Health Psychology. 2024. Vol. 24. No. 3. P. 14 https://doi.org/10.1016/j.ijchp.2024.100502
  28. Baquero-Tomás M. et al. Meaning in life as a protective factor against depression // Frontiers in Psychology. 2023. Vol. 14.
  29. Luo S. et al. The correlation between resilience and mental health of adolescents and young adults: a systematic review and meta-analysis //Frontiers in Psychiatry. 2025. Vol. 16. doi: 10.3389/fpsyt.2025.15365

Supplementary files

Supplementary Files
Action
1. JATS XML
Download

Согласие на обработку персональных данных

 

Используя сайт https://journals.rcsi.science, я (далее – «Пользователь» или «Субъект персональных данных») даю согласие на обработку персональных данных на этом сайте (текст Согласия) и на обработку персональных данных с помощью сервиса «Яндекс.Метрика» (текст Согласия).