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Record W1545541232 · doi:10.1177/070674370705200104

How Well Do Psychosocial Interventions Work in Bipolar Disorder?

2007· review· en· W1545541232 on OpenAlexaffvenue
Ari Zaretsky, Sakina J. Rizvi, Sagar V. Parikh

Bibliographic record

VenueThe Canadian Journal of Psychiatry · 2007
Typereview
Languageen
FieldMedicine
TopicBipolar Disorder and Treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPsychosocialPsychological interventionPsychologyBipolar disorderClinical psychologyPsychiatryMedicineMood

Abstract

fetched live from OpenAlex

OBJECTIVE: Although medication is the mainstay of treatment for bipolar disorder, several adjunctive psychosocial interventions have been manualized over the last decade. This paper's objective is to empirically evaluate the different treatment approaches. METHOD: We conducted a systematic review of the recent literature pertaining to psychosocial interventions in bipolar, using MEDLINE and PsycINFO. Bibliographies of papers were scrutinized for further relevant references. Articles published from 1999 up to and including 2006 were reviewed. Randomized controlled trials were emphasized. CONCLUSIONS: Although psychological models of bipolar disorder fail to inform the psychotherapy treatment to the same extent as in unipolar depression, manualized adjunctive, short-term psychotherapies have been shown to offer fairly consistent benefits to bipolar disorder patients. Cognitive-behavioural therapy, family-focused therapy, and psychoeducation offer the most robust efficacy in regard to relapse prevention, while interpersonal therapy and cognitive-behavioural therapy may offer more benefit in treating residual depressive symptoms.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.018
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.043
GPT teacher head0.337
Teacher spread0.294 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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

Citations76
Published2007
Admission routes2
Has abstractyes

Explore more

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