Mindfulness-Based Cognitive Therapy Reduces Symptoms of Depression in People With a Traumatic Brain Injury
Bibliographic record
Abstract
OBJECTIVE: We sought to determine if we could reduce symptoms of depression in individuals with a traumatic brain injury using mindfulness-based cognitive therapy. SETTING: The study was conducted in a community setting. PARTICIPANTS: We enrolled adults with symptoms of depression after a traumatic brain injury. DESIGN: We conducted a randomized controlled trial; participants were randomized to the 10-week mindfulness-based cognitive therapy intervention arm or to the wait-list control arm. MAIN MEASURES: The primary outcome measure was symptoms of depression using the Beck Depression Inventory-II. RESULTS: The parallel group analysis revealed a greater reduction in Beck Depression Inventory-II scores for the intervention group (6.63, n = 38,) than the control group (2.13, n = 38, P = .029). A medium effect size was observed (Cohen d = 0.56). The improvement in Beck Depression Inventory-II scores was maintained at the 3-month follow-up. CONCLUSION: These results are consistent with those of other researchers that use mindfulness-based cognitive therapy to reduce symptoms of depression and suggest that further work to replicate these findings and improve upon the efficacy of the intervention is warranted.
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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 | 0.000 | 0.002 |
| 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 | 0.001 |
| 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".