THE REVOLUTION IN COUPLE THERAPY: A PRACTITIONER‐SCIENTIST PERSPECTIVE
Bibliographic record
Abstract
This article offers an overview of the expanding field of couple therapy, focusing on what the author considers to be new and even revolutionary in this field. In terms of outcome research, this article suggests that differential treatment effects are discernable. Emotionally focused therapy (EFT) appears to demonstrate the best outcomes at present. The most significant differences between research studies and everyday clinical practice may be the levels of therapist supervision rather than the essential nature of clients. The manualization of treatment is also viewed positively in this review. Areas of growth are the mapping of the territory of distress, understanding the process of change, couple therapy as an effective intervention for "individual" disorders, and the integration into couple therapy of clinical research, such as the research on gender and responses in therapy, and research on adult attachment. Practitioner-scientists can contribute to this evolving field by systematic observation and by reminding researchers of the need for clinical relevance. Couple therapy is now integrating description, prediction and explanation. As a result, theory, practice and systematic investigation are beginning to create a coherent whole.
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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".