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Record W2127742161 · doi:10.1037/a0014653

Maintenance of gains following experiential therapies for depression.

2009· article· en· W2127742161 on OpenAlexaff
J. Ellison, Leslie S. Greenberg, Rhonda N. Goldman, Lynne Angus

Bibliographic record

VenueJournal of Consulting and Clinical Psychology · 2009
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsYork University
FundersNational Institute of Mental Health
KeywordsPsychologyDistressClinical psychologyPsychological interventionDepression (economics)AsymptomaticBrief psychotherapyPsychiatryPsychotherapistMedicineInternal medicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.854
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.112
GPT teacher head0.522
Teacher spread0.410 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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".

Quick stats

Citations90
Published2009
Admission routes1
Has abstractyes

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