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A Statewide Randomized Controlled Trial to Compare Three Models for Implementing Parent Child Interaction Therapy
Amy D Herschell1,2, David J Kolko2, Ashley T Scudder3
1Community Care Behavioral Health Organization, UPMC Insurance Services Division.
Objective:
This study (NIMH RO1 MH095750; ClinicalTrials.gov Identifier: NCT02543359) evaluated the effectiveness of three training models to implement a well-established evidence-based treatment, Parent-Child Interaction Therapy (PCIT).
Method:
Fifty licensed outpatient clinics, including 100 clinicians, 50 supervisors, and 50 administrators were randomized to one of three training conditions: 1) Learning Collaborative (LC), 2) Cascading Model (CM) or 3) Distance Education (DE). Data to assess training and implementation outcomes were collected at 4 time points coinciding with the training period: baseline, 6- (mid), 12- (post), and 24-months (1-year follow-up).
Results:
Multi-level hierarchical linear growth modeling was used to examine changes over time in training outcomes. Results indicate that clinicians in CM were more likely to complete training, reported high levels of training satisfaction and better learning experiences compared to the other training conditions. However, supervisors in the LC condition reported greater learning experiences, higher levels of knowledge, understanding of treatment, and satisfaction compared to supervisors in other conditions. Although clinicians and supervisors in the DE condition did not outperform their counterparts on any outcomes, their performance was comparable to both LC and CM in terms of PCIT use, supervisor perceived acceptability, feasibility, system support, and clinician satisfaction.
Conclusions:
Through the use of a randomized controlled design and community implementation, this study contributes to the current understanding of the impact of training design on implementation of PCIT. Results also indicate that although in-person training methods may produce more positive clinician and supervisor outcomes, training is not a one-size-fits-all model, with DE producing comparable results on some variables.
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