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抑郁的认知分析治疗中治疗联盟变化的轨迹。

Trajectories of change in the therapeutic alliance during Cognitive Analytic Therapy for depression.

机构信息

Department of Psychology, University of Zurich, Switzerland.

Clinical Psychology Unit, Department of Psychology, University of Sheffield, UK.

出版信息

Psychol Psychother. 2021 Sep;94(3):464-480. doi: 10.1111/papt.12322. Epub 2021 Jan 15.

DOI:10.1111/papt.12322
PMID:33448617
Abstract

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.

摘要

背景

在认知分析疗法(CAT)中,管理联盟被认为是一种核心能力和中心治疗变化过程。本研究考察了联盟变化的潜在轨迹及其与抑郁治疗结果的关系。

设计

随机对照试验的二次分析。

方法

在 8 节 CAT 干预期间,对 n=79 名抑郁参与者完成了标准化联盟(WAI-SF)和抑郁症状测量(PHQ-9)的每一次会议。应用增长混合模型来建立联盟轨迹模型,并将病例分为不同的潜在类别。使用分层线性回归,控制混杂因素,检验联盟类别与治疗后 PHQ-9 评分之间的关联。

结果

有两种联盟轨迹类别。大多数(91%)显示出稳定的联盟轨迹,而少数(9%)则在治疗过程中,初始联盟评级较差,但显著改善。基线严重程度和抑郁症状的早期变化显著预测治疗结果,但早期联盟和纵向联盟变化则没有。

结论

在控制初始症状严重程度和早期变化后,联盟轨迹并没有显著预测抑郁治疗结果。一个重要的限制是样本量小,因此需要在更大的样本中进行复制。

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