Mahavakyam Meditation: a trauma-informed practice to cultivate wellness & resilience with social work students in the classroom and beyond
Bibliographic record
Abstract
Sustaining the longevity of helping professionals’ careers requires equipping them as students with adequate paradigms and practices for mind, body, and spirit wellbeing. Thus, integrating holistic approaches into core curriculum is essential to support the preparation for spiritually informed, standardized, ethical care. The purpose of this qualitative research project was to inquire into the inspiration of Mahavakyam Meditation practice, adapted from Master Teacher Sen’s Ancient Secrets of Success system. Findings elucidate how Mahavakyam Meditation influences social work students’ self-awareness, resilience, and wellbeing as an intervention tool. The Vedic science of thought-word creation is contemplated. The authors excavated six themes through data analysis: reduced anxiety and stress, increased concentration, deepened self-awareness, decreased negative self-talk and greater self-compassion, enhanced relationships, and spiritual connection. This inquiry offers a spiritual trauma-informed practical tool to support helping professional students through their studies and practice. We recommend further research to explore the effective implementation of Mahavakyam Meditation into helping professional curriculum as well as clinical and community interventions to best support students and clinicians to build resilience when they endure their own traumatic experiences.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".