Therapist-Client Agreement in Assessments of Clients' Functioning

Eran Bar-Kalifa, Dana Atzil-Slonim, Eshkol Rafaeli, Tuvia Peri, Julian Rubel, Wolfgang Lutz

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

Objective: We aimed to assess the extent to which therapists' reports of client functioning track their clients' changing experience of their own functioning from session to session (temporal congruence) as well as the extent to which they over-or underestimate their clients' functioning (level or directional bias) and to examine whether these indices predict treatment outcomes. Method: The participants included 384 clients who were treated by 77 therapists. Both clients and therapists rated the clients' functioning each session. The clients also completed pre-and posttreatment outcome measures. Results: Using multilevel modeling, we found that therapists' reports regarding their clients' functioning tended to be temporally congruent from session to session with their clients' reported functioning. In addition, on average, therapists did not show a level bias (i.e., did not over-or underestimate their clients' functioning). Finally, temporal congruence (but not level bias) predicted better treatment outcomes. Conclusion: These findings highlight the importance of tracking clients' fluctuating symptoms over time. Thus, we discuss their implication for the policy and practice of providing session-by-session feedback to therapists.

Original languageEnglish
Pages (from-to)1127-1134
Number of pages8
JournalJournal of Consulting and Clinical Psychology
Volume84
Issue number12
DOIs
StatePublished - 1 Dec 2016

Bibliographical note

Publisher Copyright:
© 2016 American Psychological Association.

Funding

This research was supported by the German Research Foundation (DFG, LU 660/8-1 and LU 660/10-1 to Wolfgang Lutz).

FundersFunder number
Deutsche ForschungsgemeinschaftLU 660/10-1, LU 660/8-1

    Keywords

    • Congruence
    • between-and within-dyad analysis
    • outcome
    • truth and bias model

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