scholarly journals A Machine Learning Approach to Identify Socio-Economic Factors Responsible for Patients Dropping out of Substance Abuse Treatment

2020 ◽  
Vol 8 (5) ◽  
pp. 140-146
Author(s):  
Prateek Gautam ◽  
Pradeep Singh
2011 ◽  
Vol 39 (3) ◽  
pp. 257-271 ◽  
Author(s):  
Katja Kuusisto ◽  
Vesa Knuuttila ◽  
Pekka Saarnio

Background: Common factors are important for the therapy outcome and also mediate the specific factors of therapy. As one of the common factors, client's expectations towards treatment have been understudied. Aims: The aim was to examine the pre-treatment expectations of outpatient substance abuse treatment clients (N = 327, 111 females, 216 males) and its impact on retention, effectiveness and satisfaction at 6-month follow-up. Method: Dependent variables included the clients’ attitudes towards the twelve-step principles and participation, medical treatment and therapists’ role. Results: An emphasis on the importance of medical treatment at baseline predicted dropping out. Similarly, it predicted a lower percent days abstinent (PDA6) at 6 months follow-up in comparison to those who did not consider medical treatment important for recovery. Percent days abstinent increased with a more positive attitude to mutual support. At follow-up, those who had assessed the therapist's role in recovery to be most important at the baseline were most satisfied with the treatment. Conclusions: The client's pre-treatment expectations have an impact on treatment retention and effectiveness. Further effort should be made to study how clients’ image of treatment could be improved and also how the commitment of the clients with multiple problems could be improved.


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