Emotion-focused Therapy for youth: clinical outcomes of a single-site, randomized waitlist-controlled trial
Bibliographic record
Abstract
Although there is considerable evidence to support Emotion Focused Therapy (EFT) in the adult population, it has not been empirically studied in youth. EFT does not rely on an individual’s adherence to manualized strategies or between-session homework tasks, focusing instead on within-session processes. These processes activate and transform core emotions that are often linked to experiences with primary caregivers, family members, and other significant relationships. This novel study reports on an adaptation of EFT for youth. Building upon the evidence base for EFT with adults, we examined the impact of an 8-session EFT-Y intervention on clinical disorders in youth (n = 43). Using a single-site randomized waitlist-controlled design, outcomes from a treatment group (n = 22) were compared to a waitlist control group (n = 21), similar in age and presenting problems. In comparison to youth in the waitlist control group, the treatment group reported a significant decrease in their emotion dysregulation and depression scores at the end of treatment. Youth in the treatment group also demonstrated a decrease in their emotion difficulties, conduct problems, and overall difficulties, with baseline scores controlled for. These findings provide preliminary evidence that EFT-Y is an efficacious therapeutic approach for common child and adolescent psychopathology.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".