Bibliographic record
Abstract
Trauma exposure is universal to the human condition, with many affected individuals experiencing either posttraumatic stress disorder (PTSD) or subthreshold manifestations. Both scenarios can become functionally debilitating and collectively lay a heavy burden on individuals and society. Yoga nidra is one adjunctive treatment of growing interest, holding potential for its ability to alleviate symptoms of trauma, including hypervigilance, sleep disturbances, and disembodiment. However, yoga nidra practices can have re-traumatizing side-effects if not delivered conscientiously. For instance, adverse reactions such as overwhelming flashbacks, emotional distress, and extended dissociation have been reported as a result of yoga nidra practice. To prevent harm and maximize yoga nidra's therapeutic potential, 10 key components of trauma-informed yoga nidra practice are presented: (1) safe(r) and comfortable environment; (2) personal autonomy, healthy boundaries, and consent; (3) skillful mindful awareness; (4) appropriate length and preparation; (5) adequate settling and externalization; (6) sleep permission; (7) self-chosen intention; (8) flexible rotation of consciousness and breath awareness; (9) embodied pairs of opposites; and (10) conscientious visualizations. These measures protect those living with trauma and have extended benefits for nonclinical populations as well. That said, some of these components may be adapted based on context, especially in settings where direct feedback is readily available and can be prioritized. Ultimately, thoughtful decisions must be made with the intention of optimizing the safety of and benefit to the practitioners under the facilitator's care.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".