Cognitive‐behavioral therapy: applications for the management of bipolar disorder
Bibliographic record
Abstract
OBJECTIVES: This paper reviews cognitive-behavioral therapy (CBT) for bipolar disorder (BD). Data on the poor outcome of about 50% of patients diagnosed with BD supports the addition of a psychosocial intervention for the treatment of this recurring disorder. The psychoeducational nature of CBT, the effectiveness of CBT in increasing compliance to pharmacological treatment, and the ability of CBT to prevent relapse in unipolar depression (UD) are well suited to the treatment of BD. METHOD: Psychosocial interventions for BD will be briefly reviewed. Individual and group CBT interventions (published and unpublished) will also be reviewed. The significance of comorbid anxiety disorders regarding response to treatment will also be discussed. A review of the treatment protocol with the specific cognitive-behavioral interventions as applied to BD will be presented. Finally, a case example will be presented to illustrate the application of CBT to BD. RESULTS: Preliminary results indicate that CBT may be an effective adjunctive, intervention for the treatment of BD. Specifically CBT may be helpful in increasing compliance, improving quality of life and functioning, help early symptom recognition, decrease relapse and decrease depressive symptomatology. CONCLUSIONS: Preliminary data on CBT for BD are promising but more rigorous randomized clinical trials are needed to confirm the efficacy of CBT for BD. An other area of research should be to pursue the understanding of cognitive processes in BD which would allow us to refine and develop CBT interventions unique to this disorder.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".