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Record W4221033054 · doi:10.3389/fpubh.2022.730644

Use of Equine-Assisted Services to Improve Outcomes Among At-Risk and Indigenous Youth: A Scoping Review

2022· review· en· W4221033054 on OpenAlexafffundabout
Laurie Haig, Kelly Skinner

Bibliographic record

VenueFrontiers in Public Health · 2022
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of Waterloo
FundersUniversity of WaterlooSocial Sciences and Humanities Research Council of CanadaOntario Trillium Foundation
KeywordsIndigenousPopularityPositive Youth DevelopmentMental healthMedicineGerontologyPolitical sciencePsychologyPsychiatryDevelopmental psychologySocial psychology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.961
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.113
GPT teacher head0.411
Teacher spread0.299 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations8
Published2022
Admission routes3
Has abstractyes

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