Mindfulness-based cognitive therapy: benefits in reducing depression following a traumatic brain injury.
Bibliographic record
Abstract
CONTEXT: Current therapies for traumatic brain injury (TBI) include pharmacotherapy, psychotherapy, and cognitive rehabilitation. Unfortunately, psychological and emotional issues regularly go untreated in individuals with TBI even after they receive treatment for physical, behavioral, and cognitive issues. Mindfulness-based cognitive therapy (MBCT) may offer new rehabilitation opportunities for individuals with TBI. OBJECTIVE: To demonstrate the efficacy of MBCT in the treatment of clinically diagnosed depression in a TBI population. DESIGN: The research team measured depression, pain frequency and intensity, energy levels, health status, and function preintervention and postintervention. SETTING: The research team conducted the study at the Ottawa Hospital Rehabilitation Centre, Ontario, Canada. PARTICIPANTS: The research team recruited 23 participants from two sources: (1) the brain injury program at the hospital and (2) the local head-injury association. Twenty participants completed the study. INTERVENTION: The intervention was 8 weeks in length, with a 90-minute MBCT session once a week. The research team based the specific content of the study's intervention on a combination of Kabat-Zinn's manualized mindfulness-based stress reduction program and Segal and colleague's manual for MBCT. OUTCOME MEASURES: The research team determined statistical significance using paired t-tests for continuous outcomes and the McNemar chi-square test for dichotomous categorical outcomes. They also calculated effect sizes for all depression measures. RESULTS: Postintervention, the study found that MBCT significantly reduced (P < .050) depression symptoms on all scales compared to baseline. The study demonstrated medium to large effect sizes for each depression measure. Participants indicated reduced pain intensity (P = .033) and increased energy levels (P = .004). No significant changes occurred in anxiety symptoms, pain frequency, and level of functioning postintervention. CONCLUSION: MBCT was efficacious in reducing depression in the TBI population, providing ample rationale for further research with more robust designs. This study marks an important step toward the development and provision of MBCT on a wider scale to support the rehabilitation efforts of people who have depression symptoms following TBI.
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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 | 0.001 |
| 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) | 0.003 | 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".