Exploring Dance/Movement Therapy to Treat Women with Posttraumatic Stress Disorder
Bibliographic record
Abstract
Traumatic events can have significant physical, psychological, and neurological effects on an individual. Posttraumatic stress disorder (PTSD) is a psychological condition that can result from experiencing or witnessing a traumatic event. Women have a higher risk of PTSD than men do, and because PTSD has been shown to increase the risk of suicidal ideations and behavior, homicidal behavior, and general violence in the community and in the home, women are at a great risk (Levine & Land, 2014). This paper explores the use of dance/movement therapy (DMT) as an intervention to treat women suffering with PTSD. Examining the connection between the body, the mind, and the brain for individuals who have experienced traumatic events helps to highlight how multifaceted treatment methods for PTSD, such as DMT, can be more effective. Semi-structured phone interviews were conducted with 15 dance/movement therapists about the use of DMT with women experiencing PTSD. Using methods rooted in content and thematic analysis, the present study examined the emergent theme of intervention tools and tactics. The results highlight the core elements of the intervention that may be integrated into social work practice, in an effort to better support women with PTSD.
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 | 0.002 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".