Exploring the Experiences of Multiple Perspectives Within Equine-Assisted Programs Using Photovoice
Bibliographic record
Abstract
The purpose of this study was to explore the motivations and experiences of people who participated in various equine-assisted therapeutic programs. There is a need to broaden and explore the range of recreational services available to individuals with neurodevelopmental conditions and their families. The present study collaborated with TROtt, a therapeutic riding barn based in Ottawa, to collect data as a measure of their quality assurance. Participants of the current study included two instructors, one equine-assisted learning facilitator, two adult volunteers, one staff member, and one facilitator for an equine-assisted social group for young adults on the spectrum. The study used a qualitative method called Photovoice or photoelicitation, a non-invasive and participant forward way to capture participant perspectives. Participants took photos of various aspects of their equine-sessions including horses, barn equipment, and nature and then submitted those photos with a short accompanying narrative explaining the photo’s significance to their experience. Through a reflexive thematic coding analysis, results demonstrated that equine-based programs provided many benefits to participants including feelings of autonomy, opportunities to develop new skills, finding a sense of community, positive emotions, multisensory experiences, opportunities for non-verbal communication, and a calming environment. By moving towards a better understanding of motivations, preferences, and values that drive engagement in equine-based services, our work can allow for a more comprehensive knowledge of how to better structure and program recreational services to reach broader audiences.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| 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".