Mindfulness-based cognitive therapy for depression after traumatic brain injury: responders’ characteristics
Bibliographic record
Abstract
Abstract Traumatic brain injury (TBI) can alter day-to-day life. While changes in cognition and physical function are most often cited, emotional disturbances, notably depression, are also common. For individuals who experience depression symptoms, mindfulness-based cognitive therapy (MBCT) may afford the opportunity to address these symptoms by teaching skills to mitigate negative thought patterns and foster acceptance. Yet, as with any treatment for depression, MBCT may not be the best fit for everyone. According to the literature, characteristics such as age, gender, and baseline mindfulness or pain levels have the potential to affect treatment response. While these factors have yet to be explored within a TBI sample, we must additionally consider whether possible cognitive impairment due to TBI plays a role in treatment response. Drawing from an earlier multi-site randomized controlled trial to explore the efficacy of MBCT for depression in a TBI sample, the current study examined the associations between a number of baseline factors (demographic, emotional, physical, and cognitive) and decreased depression scores post-intervention. Partial correlations adjusted for gender. Findings indicated that only higher levels of pain at baseline were associated with lesser effectiveness of the intervention. MBCT offers a good treatment option for most individuals experiencing depression following TBI. Key learning aims (1) To explore factors associated with treatment response to MBCT for depression after TBI. (2) To understand how cognitive impairment resulting from TBI need not preclude treatment response. (3) To reflect on the role of pain in treatment response.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".