Introducing the Expectations and Preference Scales for Couple Therapy (<scp>EPSCT</scp>): Development, Psychometric Evaluation, and Suggested Use in Practice and Research
Bibliographic record
Abstract
While it is known that client factors account for the largest proportion of outcome variance across treatment modalities, little is known about how clients' characteristics affect the process and effectiveness of couple therapy. To further knowledge in this area, we created a brief, practice-friendly measure, the Expectation and Preference Scales for Couple Therapy (EPSCT). Three self-report scales assess clients' Outcome expectations (e.g., I expect our relationship to improve as a result of couple therapy) and role expectations for Self (e.g., I expect to listen to my partner's concerns) and Partner (e.g., I expect my partner to blame me). Three Cognitive-Behavioral, Emotionally Focused, and Family Systems preference scales use a forced-choice format to measure the comparative strength of respondents' preferences for interventions broadly reflective of each approach. A large item pool was developed from relevant literature and clinical experience and refined based on face and content analyses with two panels of experienced couple therapists and researchers. Across four studies with 1,175 participants, the scales' internal consistency reliabilities were similar and their construct validity was supported with confirmatory factor analyses and significant correlations with several established measures, including expectation measures developed for individual psychotherapy and measures of attitudes toward professional help seeking and valuing personal growth. Across all studies, participants had stronger role expectations for themselves than their partners, although gender effects differed by sample. We discuss how to use the 15-item EPSCT in clinical practice and in future research as a predictor of couple therapy processes and outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".