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Record W4391099645 · doi:10.3390/ani14020347

An Exploratory Study into the Backgrounds and Perspectives of Equine-Assisted Service Practitioners

2024· article· en· W4391099645 on OpenAlexfundno aff
Rita Seery, Deborah L. Wells

Bibliographic record

VenueAnimals · 2024
Typearticle
Languageen
FieldMedicine
TopicDiverse Approaches in Healthcare and Education Studies
Canadian institutionsnot available
FundersQueen's University BelfastQueen's UniversityDepartment of Agriculture, Environment and Rural Affairs, UK Government
KeywordsExploratory researchService (business)Medical educationPsychologySociologyMedicineBusinessMarketingSocial science

Abstract

fetched live from OpenAlex

Equine-Assisted Services (EASs) are commonplace in today's society, but vary widely in both theoretical and practical applications. Until now, practitioners' experiences and perspectives in relation to these services have received little attention. To address this, a purpose-designed online questionnaire was distributed to EAS practitioners, exploring issues relating to the nature of the service provided, practice patterns, practitioner education, perceived knowledge, challenges faced and the future direction of these services. An analysis revealed a significant association between practitioners' backgrounds and the nature of the service offered, as well as perceived knowledge. Median EAS training received to first practice was 20 days of block release over a year; however, nearly half of the sample (42.4%) reported less training than this. Equine-specific training was more limited, with 41.5% of practitioners having no horse-relevant qualifications. The most important challenges reported by practitioners involved client and equine welfare, financial sustainability and raising awareness of EAS. This research highlights the diverse nature of EAS and also raises important challenges and possible opportunities for development. Findings suggest that more progress is needed to professionalise and legitimise the area to support and help practitioners provide the best service for all concerned.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score0.222

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.175
GPT teacher head0.411
Teacher spread0.236 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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

Citations13
Published2024
Admission routes1
Has abstractyes

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