Guidelines for Classifying Evidence-Based Treatments in Couple and Family Therapy
Bibliographic record
Abstract
Guidelines for Evidence-Based Treatments in Family Therapy are intended to help guide clinicians, researchers, and policy makers in identifying specific clinical interventions and treatment programs for couples and families that have scientifically based evidence to support their efficacy. In contrast to criteria, which simply identify treatments that "work" and have been employed in the evaluation of other psychotherapies, these guidelines propose a three-tiered levels-of-evidence-based model that moves from "evidence-informed," to "evidence-based," to "evidence-based and ready for dissemination and transportation within diverse community settings." Each level reflects an interaction between the specificity of the intervention, the strength and readth of the outcomes, and the quality of the studies that form the evidence. These guidelines uniquely promote a clinically based "matrix" approach in which the empirical support is evaluated according to various dimensions including strength of the outcomes, the applicability across cultural contexts, and demonstration of specific change mechanisms. The guidelines are offered not only as a basis for understanding the evidence for diverse clinical approaches in couple and family therapy within the systemic tradition of the field, but also as an alternative aspirational model for evaluating all psychotherapies.
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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 | 0.154 | 0.389 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.014 |
| Bibliometrics | 0.057 | 0.041 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.013 | 0.011 |
| Research integrity | 0.019 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".