Integrating Yoga into Counselling and Psychotherapy: The Path and Practice
Bibliographic record
Abstract
The present study investigates the processes of integrating yoga into Western counselling and psychotherapy practices within Canadian contexts. This was accomplished by interviewing 10 mental health professionals who integrate yoga into their counselling and psychotherapy practices. Data was analyzed using the grounded theory methods and three core themes were identified in the processes of integration. The first core theme, therapist’s preparation for integrating yoga into counselling and psychotherapy, describes the ways in which therapists prepare themselves for integrating yoga into their therapy practices. The second core theme, the therapist preparing clients to practice yoga in session, explains the ways in which therapists prepare their clients to practice yoga in counselling and psychotherapy sessions. The third core theme, the practice of integrating yoga into counselling and psychotherapy, describes the specific yoga practices and approaches that are brought into therapy practice. A mid-level theory on the process of integration is presented as the Tri-Process Model of Integrating Yoga into Counselling and Psychotherapy. This study has important implications for addressing the limitations of Western counselling and psychotherapy and provides therapists with an understanding of how to incorporate more holistic and body-oriented approaches in counselling and psychotherapy practice.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.026 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| 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".