Equine Assisted Intervention for Positive Mental Health and Wellness: An Autoethnographic Study
Bibliographic record
Abstract
This paper will present the findings of an autoethnography study exploring how Equine-Assisted Interventions (EAI) can foster positive mental health and wellness in a veterans. I personally experienced EAI benefits while conducting ten therapeutic sessions by and on myself, following by reflective journaling as the data used. I am a social worker who has suffered from several mental health conditions over the last 15 years due to elements of being a first responder and military veteran culture. The research method adopted an eclectic approach of groundwork with horses, husbandry, activities base, and a combination of grounding and mindfulness elements. The conceptual framework combined various social work theories such as attachment theory, person-in-environment, and biophilia theory. Findings of EAI sessions concluded that EAI was successful in bringing a positive mental health and wellness state to a first responder dealing with mental disorders. As the findings, five themes resulted from the reflective journaling; Emotional Suppression and Vulnerability; Expectation, Control and Self-control; Communication and Empathy; Intuition and Mindfulness; and Neuroplasticity and Hormones. This paper will finish with a discussion and implication for the social work field.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".