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Record W1501231972 · doi:10.1002/jclp.22038

Patient Interpersonal and Cognitive Changes and Their Relation to Outcome in Interpersonal Psychotherapy for Depression

2013· article· en· W1501231972 on OpenAlexaff
Samantha L. Bernecker, Michael J. Constantino, Angela M. Pazzaglia, Paula Ravitz, Carolina McBride

Bibliographic record

VenueJournal of Clinical Psychology · 2013
Typearticle
Languageen
FieldPsychology
TopicPsychotherapy Techniques and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychologyCognitionInterpersonal communicationInterpersonal psychotherapyDepression (economics)Clinical psychologyInterpersonal relationshipMultilevel modelOutcome (game theory)Cognitive therapyPsychotherapistDevelopmental psychologyPsychiatryRandomized controlled trialMedicineSocial psychology

Abstract

fetched live from OpenAlex

OBJECTIVE: Despite interpersonal psychotherapy's (IPT) efficacy for depression, little is known about its change-promoting ingredients. This exploratory study examined candidate change mechanisms by identifying whether patients' interpersonal and cognitive characteristics change during IPT and whether such changes relate to outcomes. METHOD: Patients were 95 depressed adults receiving manualized IPT. We used multilevel modeling to assess the relation between change in each interpersonal and cognitive domain and outcome. RESULTS: Across all interpersonal and cognitive variables measured, patients showed significant improvement. Unexpectedly, reduced romantic relationship adjustment was related to posttreatment depression reduction (β = 2.028, p = .008, self-rated; β = 1.474, p = .022, clinician-rated). For the other measured domains, change was not significantly associated with outcome (though changes in some interpersonal variables evidenced a trend-level relation to outcome). CONCLUSIONS: Possible reciprocal influences among IPT, depression, and romantic relationship functioning are discussed, as are implications for future research.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.847
Threshold uncertainty score0.505

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 head0.145
GPT teacher head0.516
Teacher spread0.371 · 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 designObservational
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

Citations24
Published2013
Admission routes1
Has abstractyes

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