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Trajectories of change in the therapeutic alliance during Cognitive Analytic Therapy for depression
Author(s) -
Merzhvynska Mariia,
SimmondsBuckley Melanie,
Delgadillo Jaime,
Kellett Stephen
Publication year - 2021
Publication title -
psychology and psychotherapy: theory, research and practice
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.102
H-Index - 62
eISSN - 2044-8341
pISSN - 1476-0835
DOI - 10.1111/papt.12322
Subject(s) - alliance , depression (economics) , clinical psychology , psychology , confounding , randomized controlled trial , cognition , psychotherapist , medicine , psychiatry , political science , law , economics , macroeconomics
Background Managing the alliance is considered to be a core competency and central therapeutic change process during cognitive analytic therapy (CAT). This study examined latent trajectories of change in the alliance and their relationship to depression treatment outcomes. Design Secondary analysis of a randomized controlled trial. Methods A sample of N = 79 depressed participants completed standardized alliance (WAI‐SF) and depression symptom measures (PHQ‐9) every session during an 8‐session CAT intervention. Growth mixture modelling was applied to model alliance trajectories and to classify cases into different latent classes. Associations between alliance class and post‐treatment PHQ‐9 scores were examined using hierarchical linear regression, controlling for confounders. Results There were two classes of alliance trajectories. The majority class (91%) displayed stable alliance trajectories, whilst a minority class (9%) had initially poor alliance ratings that significantly improved during treatment. Baseline severity and early change in depression symptoms significantly predicted treatment outcomes, but early alliance and longitudinal alliance change did not. Conclusions Alliance trajectories did not significantly predict depression treatment outcomes after controlling for initial symptom severity and early change. An important limitation concerns the small sample size, so future replication in larger samples is necessary.