Impact of Cognitive Behaviour Therapy for Psychosis on Quality of Life in Schizophrenia - Recent Findings
Bibliographic record
Abstract
Background: With its complex etiology and presenting symptoms, schizophrenia continues to challenge clinicians and researchers alike in finding the most effective approach to alleviating symptoms and enhancing the quality of life of those affected by it.Here we explore the impact Cognitive Behavioral Therapy for psychosis (CBTp) has on the quality of life of individuals with schizophrenia, as reported in recent randomized controlled trials.Methods: We systematically searched four databases: Web of Science, PubMed, Embase and Google Scholar.The Boolean operator "AND" was used between each keyword, and a 10-year limit was applied.The search yielded 244 articles depicting a variety of studies on the effectiveness of CBT and related therapeutic interventions.To be included in our scoping review, each study had to fit the following set criteria: CBT intervention, the population represented by individuals with schizophrenia, the study had to be a Randomized Controlled Trial (RCT).Results: Seven studies met the criteria for this scoping review.Improvement in quality of life because of a CBT intervention was found in four of these studies; no improvement was seen in two, while one of the studies did not discuss their quality of life assessment results.Conclusions: Only a small number of RCTs (7) reporting on the quality of life changes resulting from a CBT intervention could be identified in the last ten years, and were included in this review.The findings are mixed, with four studies reporting quality of life improvement posttreatment and two studies at follow-up, while three reported no improvements or did not explicitly discuss it.Although quality of life is considerably affected in individuals with schizophrenia, rigorous studies assessing the effectiveness of CBT interventions tend to focus on clinical symptoms and cognitive deficits.
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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 | 0.016 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".