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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 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.923
Threshold uncertainty score0.999

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 head0.031
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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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