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Record W4412573278 · doi:10.3390/ani15152162

Review of Collars, Harnesses, and Head Collars for Walking Dogs

2025· review· en· W4412573278 on OpenAlexafffund
Camila Cavalli, Alexandra Protopopova

Bibliographic record

VenueAnimals · 2025
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHuman-Animal Interaction Studies
Canadian institutionsUniversity of British Columbia
FundersVancouver Foundation
KeywordsMedicineBreakoutPhysical medicine and rehabilitationBusiness

Abstract

fetched live from OpenAlex

As dogs are often required to be leashed in public, guardians need to choose between various restraint devices. While using collars typically considered aversive (such as choke, prong, or electric collars) is generally discouraged due to welfare concerns, guidance is less clear when it comes to selecting among other devices such as collars, harnesses, and head collars. This review examined 21 full-text articles and two abstracts on the effects of commonly used restraint devices, aiming to offer practical guidance for guardians and identifying areas for future research. The impact of these devices was examined in terms of walking kinematics, pressure distribution on the body, and behavioural signs of stress. The findings suggest there is no one-size-fits-all device, and selection should consider the individual needs of guardian and dog. For dogs that pull, non-tightening front-clip harnesses appear to offer the best balance between discomfort and reduction in pulling. Tightening harnesses, martingale collars, and head collars can pose greater discomfort and should be used with caution. For brachycephalic breeds or when pulling is not a concern, back-clip harnesses are suitable, especially chest-strap or Y-shaped ones. Flat collars are also appropriate for dogs that do not pull as they produce the least body restriction.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.783
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.053
GPT teacher head0.447
Teacher spread0.394 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations1
Published2025
Admission routes2
Has abstractyes

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