The Theory and Efficacy of Cognitive Behaviour Therapy in Bipolar Disorder
Bibliographic record
Abstract
The previous research showed that psychological treatment is effective for people who have a mental disorder, such as bipolar disorder.Bipolar disorder is a severe disorder that the patients can have two extremes of mood episodes, which are depression and (hypo)mania.Cognitive behavioural therapy is a talking therapy that helps patients rectify their patterns of thoughts and behaviours.Nowadays, therapists would like to use cognitive behavioural therapy as an adjuvant therapy with medical treatment.Therefore, studying the theory and efficacy behind cognitive behavioural therapy is important.This review is based on previous studies, which are about cognitive behavioural therapy in bipolar disorder.Cognitive distortion is the factor that can be used in cognitive behavioural therapy, to analyze the incorrect pattern of thinking and behaving in patients with bipolar disorder.The deficits in social cognition and coping skills caused by bipolar disorder are also key factors used in cognitive behavioural therapy.Cognitive behavioural therapy is effective to treat insomnia, which is one of the symptoms of bipolar disorder.Child-and family-focused cognitive behavioural therapy is effective for patients with pediatric bipolar disorder.A limitation of previous research is that medicines could affect the actual efficacy of cognitive behavioural therapy, which are not taken into account.Plus, the lack of intervention research on life quality in bipolar disorder patients is another limitation.This review can provide advice to future research in the field of cognitive behavioural therapy and bipolar disorder.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".