Bibliographic record
Abstract
A post-traumatic response includes alterations in functioning on the physical, mental, emotional, social, and spiritual levels. Interest in using complementary therapies resulted from PTSD patient requests for modalities that address their spiritual needs. The positive neurophysiology effects on PTSD symptoms produced by spiritual practices generates renewed interest in the approaches to the psyche proposed by Carl Jung, and also in traditional cultural practices for healing trauma. In traditional worldviews the shock of trauma can cause a part of the soul to fracture off and remain trapped in a non-ordinary reality. Jung encouraged therapeutic regression to connect with the lost part. Regression therapy gained support following MRI studies showing that trauma narratives are replayed through the brain’s right hemisphere. MRI studies support research showing that right hemisphere options, such as visualization, increase the possibility for healing trauma. The purpose of this research was to determine if there were pre- and post-intervention differences when using spiritually-focused guided visualization to regress subjects to a traumatic event, there to reclaim and reintegrate a soul part that had fractured off during trauma. Eight study participants from an Indigenous Community in Canada participated. Pre-to-post score differences on the PCL-5 suggest a positive and clinically meaningful response to the intervention. The themes derived from the narrative descriptions indicate that the soul retrieval intervention increased the well-being of the study participants.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".