Measuring the psychophysiological changes in combat Veterans participating in an equine therapy program
Bibliographic record
Abstract
Introduction: This study addressed the following research questions: (1) Does horsemanship training with Veterans lead to a balance in the autonomic nervous system? and (2) Does horsemanship training with Veterans lead to a self-perceived improvement in quality of life? Methods: A total of 17 participants in 3 different cohorts participated weekly in an 8-week equine therapy experience designed to address and heal combat Veterans suffering from anxiety and other symptoms associated with post-traumatic stress disorder (PTSD). The study took place in San Diego County, California, where the Veterans worked with seasoned therapy horses and experienced riding instructors. The effectiveness of this program was quantitatively evaluated by measuring heart rate variability (HRV), and scores on the positive and negative affect schedule (PANAS). HRV was measured before, during, and after each session to determine the impact on the autonomic nervous system. The PANAS was administered before and after each session to determine self-perceived improvement in quality of life. Results: Analysis of the HRV results revealed a weekly improvement in HRV patterns at each measurement stage. The average low frequency/high frequency (LF/HF) ratio of study participants significantly decreased by 20.6% ( F=9.84, p<0.001). Poincaré plots of the participant's R-R values further demonstrated improved HRV. The average positive affect score on the PANAS significantly increased by 14.4% ( t=5.78, p<0.001) with Veterans reporting they felt less anxiety and stress. Discussion: This study provided evidence-based results that therapeutic horsemanship programs may bring psychophysiological benefits to Veterans suffering from trauma, stress and anxiety associated with PTSD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".