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Record W4387343509 · doi:10.3390/ani13193091

Insights into Canadians’ Perceptions of Service Dogs in Public Spaces

2023· article· en· W4387343509 on OpenAlexafffundabout
Maryellen Gibson, Linzi Williamson, Colleen Anne Dell

Bibliographic record

VenueAnimals · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of Saskatchewan
FundersCanadian Institutes of Health Research
KeywordsPerceptionPublic serviceService (business)GeographySociologyPublic relationsPolitical scienceBusinessPsychologyMarketing

Abstract

fetched live from OpenAlex

Service Dogs (SDs) are an increasingly common type of working dog supporting people with disabilities in Canada. One of the groups being paired with SDs is Veterans diagnosed with posttraumatic stress injuries (PTSIs). In past research, Veterans have expressed stress over negative interactions with people in public spaces because an SD brings attention to their disability. There is a dearth of research exploring perceptions of SDs in public settings. METHODS: A total of 485 Canadians were surveyed via an online questionnaire about their demographic information and beliefs about SDs in public spaces. Data were analyzed using robust ordinary least squares (OLS) regression to determine which demographic features, if any, contribute to perceptions. RESULTS: Generally, the Canadian public holds highly positive perceptions of SDs being in public spaces. Our analysis found that women, people who currently have pets, and Indigenous people were more supportive of SDs in public spaces than others. People with certain cultural heritages were less receptive. CONCLUSION: These findings are an important beginning contribution to the growing SD and Veteran health field.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.168

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0090.003
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.031
GPT teacher head0.337
Teacher spread0.305 · 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

Citations4
Published2023
Admission routes3
Has abstractyes

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