The impact of mindfulness on suicidal behavior: a systematic review
Bibliographic record
Abstract
INTRODUCTION: Mindfulness-based interventions (MBI) have been growing progressively as treatment options in the field of mental health. Aim: To assess the impact of mindfulness-based interventions for reducing suicidal thoughts and behaviors. METHODS: A systematic review was performed in December 2020 using PubMed, PsycINFO, EMBASE, SciELO, Pepsic, and LILACS databases with no year restrictions. The search strategy included the terms ('mindfulness' OR 'mindfulness-based') AND ('suicide' OR 'suicidal' OR 'suicide risk' OR 'suicide attempt' OR 'suicide ideation' OR 'suicide behavior'). The protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO), CRD42020219514. RESULTS: A total of 14 studies met all inclusion criteria and were included in this review. Most of the studies presented Mindfulness-Based Cognitive Therapy as the MBI assessed (n=10). An emerging and rapidly growing literature on MBI presents promising results in reduction of suicide risk, particularly in patients with MDD. Four studies assessing other MBI treatment protocols (Mindfulness-Based Stress Reduction; Daily Mindfulness Meditation Practice; Mind Body Awareness and Mindfulness-Based Cognitive Behavior Therapy) all demonstrated that MBI reduces factors associated with suicide risk. CONCLUSION: MBI might target specific processes and contribute to suicide risk reduction.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".