Collage as a Method of Inquiry for University Women Practicing Mahavakyam Meditation: Ameliorating the Effects of Stress, Anxiety, and Sadness
Bibliographic record
Abstract
Spiritually focused interventions are increasing within social work/helping professions practice and research. Specifically the incorporation of meditation is taking root. This article conceptualizes a particular mantra meditation system, combined with visualization, called Mahavakyam Meditation (MM), and explores how it ameliorated the stress, sadness, and anxiety for women attending university. MM differs from the application of other mantra meditations in western therapeutic approaches with its unique emphasis on the creative potential of thought. MM includes two components: repetition of four proclamations, and creating a positive mental image of a goal not yet accomplished but imagined and believed as if it were already achieved. In this qualitative arts-based exploratory study, the author adopted a collage method as a form of analysis and representation to understand university women’s experiences of applying meditation and visualization as an alternative mental health strategy. Findings indicated that participants realized a reduction of their distressing symptoms in a number of ways: a broadened perspective, the ability to reject adverse self-talk, increased awareness and feelings of tranquility, joy, and self-acceptance, an increased belief in the capacity to achieve goals, and enhanced contemplation on and motivation to fulfill those goals. The collage method was well suited to the experiential MM.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".