OBJECTIVE: Assessing self-rated items that might have an impact on clinicians recommending brief treatment (BT) over unlimited or long-term treatment (ULT). METHOD: On the basis of patient self-report data we compared patients referred by clinicians to BT (n=71) with those referred to ULT (n=145). RESULTS: The final multiple logistic regression model indicates that the chance of being allocated to BT increases with: more satisfaction with support, higher self-esteem, primary education or less, and high desire for support as an intervention. With regard to desire to confess in treatment, low and high scores make the chance of being allocated to BT lower. This is also the case for daily hassles. Finally, some specific target complaints, in particular anxiety, lower the chance of being allocated to BT. CONCLUSION: Using data about patient's complaints and symptoms, stress and support, personality and coping, and request for type of intervention, we built a regression-model that classified 80% of the patients correctly with regard to allocation to BT or ULT.
OBJECTIVE: Assessing self-rated items that might have an impact on clinicians recommending brief treatment (BT) over unlimited or long-term treatment (ULT). METHOD: On the basis of patient self-report data we compared patients referred by clinicians to BT (n=71) with those referred to ULT (n=145). RESULTS: The final multiple logistic regression model indicates that the chance of being allocated to BT increases with: more satisfaction with support, higher self-esteem, primary education or less, and high desire for support as an intervention. With regard to desire to confess in treatment, low and high scores make the chance of being allocated to BT lower. This is also the case for daily hassles. Finally, some specific target complaints, in particular anxiety, lower the chance of being allocated to BT. CONCLUSION: Using data about patient's complaints and symptoms, stress and support, personality and coping, and request for type of intervention, we built a regression-model that classified 80% of the patients correctly with regard to allocation to BT or ULT.
Authors: Mark Olfson; Ramin Mojtabai; Nancy A Sampson; Irving Hwang; Benjamin Druss; Philip S Wang; Kenneth B Wells; Harold Alan Pincus; Ronald C Kessler Journal: Psychiatr Serv Date: 2009-07 Impact factor: 4.157