Integrating Traditional Healing Methods into Counselling and Psychotherapy with Punjabi and Sikh Individuals
Bibliographic record
Abstract
Evidence-based practice goes well beyond merely matching client disorder to theoretical approach and instead entails the integration of research evidence with clinical expertise in the context of patient characteristics, culture, and preferences. For clients who are less acculturated to Canadian society or for those who still strongly identify with their cultural roots, incorporation of traditional healing methods into counselling and psychotherapy appears highly beneficial. Based on a review of the literature, this paper offers a discussion of frameworks which can guide the incorporation of traditional healing practices into counselling and psychotherapy and outlines model/theory-embedded strategies and interventions that have been reported to be effective with some Punjabi Sikh clients in peer-reviewed published outlets. This information will be useful for professionals who have limited experience with Punjabi Sikh individuals, clinical supervisors overseeing trainees providing mental health services to Punjabi Sikhs, instructors teaching cross/multicultural counselling or psychotherapy classes, and those wishing to further develop or refine existing competence. These proposed strategies and interventions should be subject to research investigations and clinically tested by practitioners to further increase confidence in their application.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".