IDEAS home Printed from https://ideas.repec.org/a/abq/ijist1/v4y2022i6p82-87.html
   My bibliography  Save this article

Psycho-Social and Morbidity of Substance Use Disorder in Women

Author

Listed:
  • Mariyam Iftikhar

    (Department of Psychology (University of Gujrat, Pakistan))

Abstract

Substance abuse disorder is a major and worldwide concern thatcursed countries and mankind. Psychosocial factors influences differ across the person and may contribute to the development of physical and mental disorders. The research aimedto investigate the impact of psychological factors (Self-esteem, Depression, Anxiety,and Decision-Making Confidence) and social factors (Childhood Problems, Hostility, Risk-taking,and Social Conformity) that predictors of substance use disorder in women. en cross-sectional survey design was used in this study. Drug Abuse Screaming Test (DAST) and psychosocial functioning scale wereused to collect data onwomen (N=200). The purposive sampling technique was employed for sample selection;moreover,the snowball technique was also used as the drug-addicted women recommended the other women. Resultsof the study ravels that psychosocial factors were a significant predictor of substance use disorder in women. The finding of the multiple regression analysis reveals that psychosocial factors were significant predictors of substance use disorder in women [R2 =.46, F (1,142)14.26, p

Suggested Citation

  • Mariyam Iftikhar, 2022. "Psycho-Social and Morbidity of Substance Use Disorder in Women," International Journal of Innovations in Science & Technology, 50sea, vol. 4(6), pages 82-87, September.
  • Handle: RePEc:abq:ijist1:v:4:y:2022:i:6:p:82-87
    as

    Download full text from publisher

    File URL: https://journal.50sea.com/index.php/IJIST/article/view/372/691
    Download Restriction: no

    File URL: https://journal.50sea.com/index.php/IJIST/article/view/372
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Hua Gong & Chuyin Xie & Chengfu Yu & Nan Sun & Hong Lu & Ying Xie, 2021. "Psychosocial Factors Predict the Level of Substance Craving of People with Drug Addiction: A Machine Learning Approach," IJERPH, MDPI, vol. 18(22), pages 1-12, November.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Najwa Farooq, 2022. "Psychosocial Factors as the Determinants of Relapse in Individuals with Substance Use Disorder," International Journal of Innovations in Science & Technology, 50sea, vol. 4(6), pages 97-104, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:abq:ijist1:v:4:y:2022:i:6:p:82-87. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Iqra Nazeer (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.