Systematic review of the effects of evidence-based psychotherapies on neurocognitive functioning in mood disorders
Bibliographic record
Abstract
OBJECTIVE: Neurocognitive impairment is considered a core feature of mood disorders. Research has shown that neurocognitive impairment often persists beyond mood symptom resolution and can have significant deleterious effects on interpersonal relationships, academic achievement, occupational functioning and independent living. As such, neurocognitive impairment has become an important target for intervention. In this systematic review, we aimed to examine the extant literature to ascertain whether current standard evidence-based psychotherapies can improve neurocognitive functioning in mood disorders. METHOD: Studies examining changes in neurocognitive functioning following evidence-based psychotherapy were identified using MEDLINE, PsycINFO and Web of Science databases. Given the heterogeneity of study procedures, treatment protocols and patient samples, a narrative rather than meta-analytic review technique was employed. RESULTS: Nineteen studies (21 articles) met inclusion criteria. There was preliminary evidence of improved executive functioning following evidence-based psychotherapy for Major Depressive Disorder and Bipolar Disorder. There was also some signal of reduced negative biases in emotional information processing following psychotherapy in depression. Due to methodological variability across studies however, it was difficult to draw clear conclusions. CONCLUSION: Findings from the current review suggest that evidence-based psychotherapies may influence some aspects of neurocognitive functioning in mood disorders. This continues to be an ongoing area of importance and warrants further research.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".