Review of Collars, Harnesses, and Head Collars for Walking Dogs
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".