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Record W2098986348 · doi:10.1002/rnj.027

Therapeutic Horse Back Riding of a Spinal Cord Injured Veteran: A Case Study

2012· article· en· W2098986348 on OpenAlexaff
Glennys Asselin, Julius H. Penning, Savithri Ramanujam, Rebecca Colina Neri, Constance Ward

Bibliographic record

VenueRehabilitation Nursing · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsSpinal Cord Injury BC
Fundersnot available
KeywordsPhysical medicine and rehabilitationMedicineHorseSpinal cordSpinal cord injuryPhysical therapyPsychologyBiologyPsychiatry

Abstract

fetched live from OpenAlex

PURPOSE: To determine an incomplete spinal cord injured veteran's experience following participation in a therapeutic horseback riding program. METHODS: Following the establishment of a nationwide therapeutic riding program for America's wounded service veterans in 2007, a Certified Rehabilitation Registered Nurse from the Michael E. DeBakey Veteran Affairs Medical Center worked with an incomplete spinal cord injured veteran who participated in the Horses for Heroes program. RESULTS: This program resulted in many benefits for the veteran, including an increase in balance, muscle strength, and self-esteem. DISCUSSION: A physical, psychological, and psychosocial benefit of therapeutic horseback riding is shown to have positive results for the spinal cord injured. Therapeutic riding is an emerging field where the horse is used as a tool for physical therapy, emotional growth, and learning. CONCLUSION: Veterans returning from the Iraq/Afghanistan war with traumatic brain injuries, blast injuries, depression, traumatic amputations, and spinal cord injuries may benefit from this nurse-assisted therapy involving the horse.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Case report · Consensus signal: Case report
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0060.001
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.047
GPT teacher head0.431
Teacher spread0.384 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designCase report
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

Citations15
Published2012
Admission routes1
Has abstractyes

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