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Record W2287815800 · doi:10.3233/978-1-61499-566-1-847

Service Dogs for People with Spinal Cord Injury: Outcomes Regarding Functional Mobility and Important Occupations

2015· article· en· W2287815800 on OpenAlexaff
Dany H. Gagnon, Lise Poissant

Bibliographic record

VenueStudies in health technology and informatics · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversité de SherbrookeCentre for Interdisciplinary Research in RehabilitationUniversité de MontréalUniversité Laval
Fundersnot available
KeywordsPsychosocialWheelchairSpinal cord injuryMedicinePhysical therapyPhysical medicine and rehabilitationManual wheelchairLongitudinal studyService (business)Spinal cordAssistive technologyRehabilitationGerontologyPsychologyPsychiatryComputer science

Abstract

fetched live from OpenAlex

No research using standardized tests based on direct observations along with longitudinal studies have shown the effects of service dogs on persons with mobility impairment. Our research objectives were to document the consequences of the use of the service dog on wheelchair propelling, grasping objects, shoulder pain, occupational performance, reintegration into normal living and psychosocial impacts for people with spinal cord injury (SCI). A cross sectional study was conducted with 45 males and 21 females with SCI (average age = 41.2). They were assessed in their homes and their communities, two to five years after they received their service dogs. Observations were based on four testing methods. An ongoing longitudinal study is reported, based on 9 months (n = 8 to 16) of data from four standardised questionnaires. Results demonstrate that services dogs are an efficient assistive technology for persons with SCI.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.072
GPT teacher head0.429
Teacher spread0.357 · 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

Citations10
Published2015
Admission routes1
Has abstractyes

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