Boxing as an Intervention in Mental Health: A Scoping Review
Bibliographic record
Abstract
Introduction: Physical activity has been shown to have a multitude of mental health benefits. However, there is limited evidence on the specific mental health benefits of boxing. We conducted a scoping review of academic and grey literature to map research of boxing exercises as an intervention in mental health and to identify gaps in knowledge. Methods: The authors utilized the PRISMA-ScR methodological approach and guidelines from the Joanna Briggs Institute and a structured search was completed from inception until August 08, 2022. Results: We identified 16 documents that used non-contact boxing as an exercise intervention that improved various mental health difficulties. Non-contact boxing exercises, usually in a high-intensity-interval training group setting, provided significant reduction in symptoms of anxiety, depression, PTSD and negative symptoms of schizophrenia. Non-contact boxing provided a cathartic release of anger and stress, with evidence of improved mood, self-esteem, confidence, concentration, metabolic burden, strength and coordination. Conclusions: Preliminary evidence indicates that non-contact boxing exercises are a promising intervention to improve mental health burden. Further well designed randomized controlled trials using group, non-contact boxing exercises as an intervention for common mental disorders are warranted to confirm its benefits for mental health.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".