The Effects of Massage Therapy on Medically Induced Trauma and Touch Aversion: A Case Report
Bibliographic record
Abstract
Almost 1 million US adults are diagnosed annually with post-traumatic stress disorder related to medical trauma. Individuals who experience life-threatening illness or injuries, frequent hospitalizations, and multiple invasive procedures are more likely to develop post-traumatic stress and touch aversion, making it difficult for them to relax and feel safe in healthcare settings. Psychological and somatic symptoms can complicate recovery and decrease quality of life. While massage has been shown to offer a variety of physical and psychological benefits, little is known about the benefits of massage for those diagnosed with post-traumatic stress and touch aversion related to medical trauma. A 44-year-old female was referred to massage therapy for muscle pain and generalized weakness, symptoms of a chronic degenerative illness with limited treatment options. Complicated by multiple diagnoses, her long-standing anxiety and depression had worsened, and she suffered from post-traumatic stress and touch aversion due to significant medical trauma. The patient's goals included relaxation, decreased pain and anxiety, as well as improvements in her aversion to touch when receiving necessary medical care. A wide variety of massage techniques were offered based on the patient's physical and psychological symptoms, and her receptivity to touch. Over the course of 2 years, the patient's anxiety and distress decreased as her ability to communicate her needs increased. A trauma-informed approach is essential when providing massage for those with post-traumatic stress and touch aversion from medical trauma. A pre-massage consultation and customization of the massage allowed the patient to provide consent and have control over where and how her body was touched, something that is often not possible with medical procedures. Further research is needed to determine how best to provide massage therapy to these individuals and measure outcomes related to effectiveness and symptom improvement.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".