Therapist Responsiveness in Couples Therapy: Perceptions of Male and Female Partners
Bibliographic record
Abstract
Responsiveness refers to the ability of the therapist to understand a client’s concerns and convey this understanding through a thoughtfully selected intervention, treatment, or other action. Although a sizeable literature on individual therapist responsiveness exists, a complementary framework to guide responsiveness in couples therapy is absent. This is surprising given that responsiveness becomes appreciably more complex when the therapist must attend to the moment-to-moment needs of both partners and tailor interventions to fit the needs of each individual as well as the couple. The present study sought to understand how individual partners experience therapist responsiveness in the context of couples therapy. One-on-one semi-structured interviews were conducted with eight individuals from four partnerships currently in couples therapy. Transcripts were coded and analyzed following constructivist grounded theory. A hierarchical model of the experience of therapist responsiveness in couples therapy emerged and subsumed a number of categories. Clear differences between male and female partners were inconclusive. By extrapolating principles from this model, a theory was proposed that identifies factors and processes that contribute to the experience of therapist responsiveness in couples therapy. Limitation and directions for future research are presented.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".