Equine-Assisted Therapy for Posttraumatic Stress Disorder Among First Responders
Bibliographic record
Abstract
Equine-assisted therapy has emerged as an adjunctive integrative health modality in treating individuals experiencing physiological and psychological distress. However, limited research exists to assess the efficacy of such treatments as a possible adjunct to psychological treatment for Posttraumatic Stress Disorder (PTSD) in first responders. The current pilot study examines the additive benefits of equine-assisted exposure for first responders suffering occupational incapacitation from operational-related trauma. Seven first responders participated in an 8-week, 90-minute, equine-assisted therapy program. Primary outcome measures (i.e., anxiety, depression, trauma, inflexibility and avoidance) were administered pre- and post-intervention. Additional measures examined feelings about the self and views towards aspects of the program. Findings suggested initial support for symptom reduction, particularly for depressive and trauma-related symptoms. Qualitative feedback from participants suggested significant benefits including increased sense of peace, reduced anxiety, mindfulness, and increased trust in the self and others. To our knowledge, this is the first study to directly examine clinical outcomes of first responders with PTSD participating in equine-assisted therapy and presents a promising adjunct to care in first responders moving forward.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".