Mindfulness Intervention Mitigates Trauma-Induced Cognitive Decline Among Healthcare Professionals
Bibliographic record
Abstract
Background: Healthcare professionals are vulnerable to trauma-induced cognitive decline due to their exposure to traumatic events in the workplace. Mindfulness-based interventions have shown promise in mitigating stress and improving cognitive function. This study aimed to investigate the impact of a mindfulness intervention on trauma-induced cognitive decline among healthcare professionals. Methodology: A comparative cohort study was conducted with 54 participants randomly assigned to intervention (n=25) and control (n=24) groups. The intervention group received an eight-week mindfulness program, while the control group received no intervention. Cognitive function, burnout, and perceived stress were assessed using pre- and post-intervention standardized measures. Results: The intervention group demonstrated significant improvements in cognitive function, evidenced by increased Montreal Cognitive Assessment (MOCA) scores (p < 0.01). Additionally, significant reductions were observed in emotional exhaustion and depersonalization scores, along with decreased perceived stress levels (p < 0.01). The control group showed marginal improvements in cognitive function but experienced a significant increase in depersonalization (p < 0.05). Both groups exhibited reduced perceived stress post-intervention. Conclusion: The findings suggest that mindfulness practices effectively prevent cognitive impairment in trauma patients and enhance their cognitive and emotional well-being.
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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 | 0.000 |
| 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".