Use of Equine-Assisted Services to Improve Outcomes Among At-Risk and Indigenous Youth: A Scoping Review
Bibliographic record
Abstract
Equine-assisted services (EAS) are gaining popularity as ways to promote psychological health and social well-being. EAS may show particular promise as culturally appropriate initiatives for at-risk Indigenous youth, as they are thought to align well with Indigenous ways of knowing which place emphasis on relationships between the land and all living beings. We seek to better understand previous uses of EAS as initiatives for at-risk youth populations, including Indigenous populations, and learn about which outcomes have been addressed in the literature with an EAS initiative by conducting a scoping review. The review focused on initiatives targeting at-risk youth aged 10-18 years of age in Canada, Australia, New Zealand, and the United States. A total of 27 studies were included in the final analysis from all target countries except New Zealand. The target populations were further divided into four subgroups: at-risk youth, youth with mental health disorders and/or learning disabilities, youth survivors of trauma/abuse, and at-risk Indigenous youth. Overall findings of the review suggest EAS are promising approaches for achieving therapeutic and learning goals with the potential to be successful with both Indigenous youth and at-risk youth more broadly.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".