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Record W2143070239 · doi:10.1080/j003v13n02_03

Service Dogs: A Compensatory Resource to Improve Function

2001· article· en· W2143070239 on OpenAlexaboutno aff
Stacey K. Fairman, Ruth A. Huebner

Bibliographic record

VenueOccupational Therapy In Health Care · 2001
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsnot available
Fundersnot available
KeywordsService (business)ReferralOccupational therapyTerminologyAnimal-assisted therapyMedicineFunction (biology)PsychologyPhysical therapyNursingBusinessAnimal welfarePet therapyMarketing

Abstract

fetched live from OpenAlex

Objective. This study examined the physical, emotional, social, and economic functions of service dogs, the training methods for service dog/owner teams, and problems encountered with service dogs in relationship to occupational therapy literature and domain of concern. Method. A 31-question survey was developed based on the literature and Uniform Terminology (AOTA, 1994) and was completed by 202 service dog owners from 40 states and Canada. Results. Owners reported that service dogs assisted them in 28 functional tasks, helped them to feel safe, increased their social interaction, and reduced physical assistance by others. Problems with service dogs included difficulty with dog maintenance and public awareness of their role as a worker or assistant to the owner. Over 80% of respondents desired additional training in alternative ways to perform daily living tasks. Conclusion. The use of service dogs is consistent with the occupational therapy domain of concern and practice. Occupational therapists might collaborate with service dog trainers and potential owners in referral, assessment, training, and follow-up services.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0070.001

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 head0.039
GPT teacher head0.392
Teacher spread0.353 · 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 designObservational
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

Citations74
Published2001
Admission routes1
Has abstractyes

Explore more

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