A Practitioner’s Guide to Breathwork in Clinical Mental Health Counseling
Bibliographic record
Abstract
Breathwork techniques and therapies offer a set of practical interventions for clinical mental health counselors (CMHCs) and are viable methods for integrating physiological sensitivities in treatment by way of the relaxation response. We discuss an organizing framework of breathwork practices and identify three broad categories of breathwork within the field: deep relaxation breathing, mindfulness breathwork, and yogic breathing. Each style is distinct in how it is applied and in the specific respiratory patterns that users are instructed to use. We also aim to elaborate the physiological effects, clinical research outcomes, and applicability of breathwork for treating mental illness. Overall, research findings indicate that breathwork may be efficacious for treating anxiety, depression, and posttraumatic stress disorder. Despite preliminary evidence for breathwork’s efficacy for treating common psychological distress, more research is needed to evaluate its utility for treating a wider range of mental illness. CMHCs are encouraged to incorporate breathwork techniques in their clinical treatment programs but must appraise the value of each technique individually.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.070 | 0.049 |
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".