Pilot evaluation of a mindfulness-based intervention to improve quality of life among individuals who sustained traumatic brain injuries
Bibliographic record
Abstract
PRIMARY OBJECTIVE: To examine the potential efficacy of a mindfulness-based stress reduction approach to improve quality of life in individuals who have suffered traumatic brain injuries. RESEARCH DESIGN: Pre-post design with drop-outs as controls. METHODS AND PROCEDURES: We recruited individuals with mild to moderate brain injuries, at least 1 year post-injury. We measured their quality of life, psychological status, and function. Results of 10 participants who completed the programme were compared to three drop-outs with complete data. EXPERIMENTAL INTERVENTION: The intervention was delivered in 12-weekly group sessions. The intervention relied on insight meditation, breathing exercises, guided visualization, and group discussion. We aimed to encourage a new way of thinking about disability and life to bring a sense of acceptance, allowing participants to move beyond limiting beliefs. MAIN OUTCOMES AND RESULTS: The treatment group mean quality of life (SF-36) improved by 15.40 (SD = 9.08) compared to - 1.67 (SD = 16.65; p = 0.036) for controls. Improvements on the cognitive-affective domain of the Beck Depression Inventory II (BDI-II) were reported (p = 0.029), while changes in the overall BDI-II (p = 0.059) and the Positive Symptom Distress Inventory of the SCL-90R (p = 0.054) approached statistical significance. CONCLUSIONS: The intervention was simple, and improved quality of life after other treatment avenues for these participants were exhausted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".