Premium
Complex individual pathways or standard tracks? A data‐based discussion on the trajectories of change in psychotherapy
Author(s) -
Schiepek Günter,
Gelo Omar,
Viol Kathrin,
Kratzer Leonhard,
Orsucci Franco,
Felice Giulio,
StögerSchmidinger Barbara,
Sammet Isa,
Aichhorn Wolfgang,
Schöller Helmut
Publication year - 2020
Publication title -
counselling and psychotherapy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.38
H-Index - 32
eISSN - 1746-1405
pISSN - 1473-3145
DOI - 10.1002/capr.12300
Subject(s) - outcome (game theory) , session (web analytics) , gold standard (test) , perspective (graphical) , psychology , dynamics (music) , range (aeronautics) , nonlinear system , trajectory , process (computing) , series (stratigraphy) , computer science , cognitive psychology , econometrics , statistics , artificial intelligence , mathematics , engineering , mathematical economics , paleontology , physics , quantum mechanics , astronomy , world wide web , biology , aerospace engineering , operating system , pedagogy
Objective Current approaches of routine outcome monitoring (session‐by‐session measures) expect that trajectories of change should move on a standard track. Patients moving out of standard tracks are assumed to be at risk of deterioration. From a nonlinear dynamic systems perspective, there is not any assumption regarding a supposed standard track a patient should follow. Individual trajectories should be more complex than averaged tracks, highly individual, and characterised by pattern transitions. Method We tested if high‐frequency (daily) trajectories of change are moving on standard tracks, if there are different complexity levels of high‐ versus low‐frequency time series, if ‘not on track' dynamics will be correlated with poor outcome and if complexity peaks representing the critical instabilities of a process will be correlated with the outcome. The patients included in the data analysis ( N = 88) used the Therapy Process Questionnaire (TPQ) for daily self‐assessments and the ICD‐10‐based Symptom Rating (ISR) for outcome evaluation. Results High‐frequency trajectories are not running on standard tracks and are not necessarily correlated with poor outcome. Locally increased complexity may be associated with good outcome. Conclusion It may be useful to move beyond the concept of standard tracks and expected treatment outcomes. Routine feedback procedures should use the information that is given by the nonlinear dynamics of multiple change criteria.