Psychoéducation et traitements cognitifs et comportementaux du trouble bipolaire
Bibliographic record
Abstract
UNLABELLED: Bipolar disorder is a severe mood disorder characterized by recurrence of mania and depression. Despite the use of mood stabilizers, a significant proportion of bipolar patients experience relapse, psychosocial impairment and persistent symptoms. A significant part of patients show poor adhesion to the pharmacological treatment. This article aims to provide an overview of research focusing on psychoeducational and cognitive-behavioral treatment (CBT) of bipolar patients. METHOD: Studies were identified through Medline searches between 1971 and 2005. RESULTS: Studies on bipolar patients suggest that psychoeducational interventions may improve treatment adherence, illness knowledge, ability to cope with early manic symptoms and tend to reduce the risk of manic relapses. CBT tends to diminish depressive symptoms, improve treatment adherence and reduce the risk of depressive and manic relapses. Most psychoeducational and CBT studies share a common medical model of the illness, thereby making clear distinctions of impact of each intervention difficult. Few studies focused on patients with problems with mood stabilizers adherence. It is now important to develop specific interventions for those patients. CONCLUSION: According to these studies, bipolar patients are likely to benefit from psychoeducational or CBT interventions added to usual pharmacotherapy. In order to overcome limitations of existing research, future studies should adjust for the effect of pharmacological treatment, the type and severity of psychopathology at baseline, the acceptance of and the adaptability to the illness and it's awareness.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".