A novel framework for ketamine-assisted couple therapy
Bibliographic record
Abstract
Intimate relationship distress is prevalent and is associated with poorer health, mental health, and mortality outcomes. Evidence-based couple therapies target cognitive, behavioral, and emotional processes that underlie relationship dysfunction. Increasing research and clinical evidence supports the efficacy of ketamine-assisted psychotherapy (KAP) for addressing clinical mental health concerns, including depression, anxiety disorders, posttraumatic stress disorder, and more. The purported mechanisms of KAP are also likely to improve psychosocial and relational functioning for patients and may be useful for supporting change mechanisms in couple therapy. This paper reviews the current evidence for therapeutic ketamine and KAP and outlines how the mechanisms of ketamine therapy may also augment the cognitive, behavioral, and emotional interventions in the most commonly used evidence-based couple therapies. Key mechanisms include increased neuroplasticity, changes in functional connectivity, adaptive dissociation, decreased inhibition, and reduced avoidance. Given the reciprocal interaction between relationship dysfunction and mental health problems, ketamine may also help alleviate relationship distress by directly treating clinical mental health symptoms. We then outline a proposed framework for ketamine-assisted couple therapy, addressing the application of KAP preparation, dosing, and integration to a dyadic intervention framework in a way that can be applied to different couple therapy modalities. This clinical framework for couples' KAP may be useful for clinicians and researchers working to improve the efficacy of couple therapy, particularly when one or both partners has accompanying mental health concerns.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".