Maintenance of gains following experiential therapies for depression.
Bibliographic record
Abstract
Follow-up data across an 18-month period are presented for 43 adults who had been randomly assigned and had responded to short-term client-centered (CC) and emotion-focused (EFT) therapies for major depression. Long-term effects of these short-term therapies were evaluated using relapse rates, number of asymptomatic or minimally symptomatic weeks, survival times across an 18-month follow-up, and group comparisons on self-report indices at 6- and 18-month follow-up among those clients who responded to the acute treatment phase. EFT treatment showed superior effects across 18 months in terms of less depressive relapse and greater number of asymptomatic or minimally symptomatic weeks, and the probability of maintaining treatment gains was significantly more likely in the EFT treatment than in the CC treatment. In addition, follow-up self-report results demonstrated significantly greater effects for EFT clients on reduction of depression and improvement of self-esteem, and there were trends in favor of EFT, in comparison with CC, on reduction of general symptom distress and interpersonal problems. Maintenance of treatment gains following an empathic relational treatment appears to be enhanced by the addition of specific experiential and gestalt-derived emotion-focused interventions. Clinical and theoretical implications of these findings 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".