Geographic Availability of Assistance Dogs: Dogs Placed in 2013-2014 by ADI- or IGDF-Accredited or Candidate Facilities in the United States and Canada, and Non-accredited U.S. Facilities.
Bibliographic record
Abstract
Assistance dogs' roles have diversified to support people with various disabilities, especially in the U.S. Data presented here are from the U.S. and Canada non-profit facilities (including both accredited and candidate members that fulfilled partial requirements: all here termed "accredited") of Assistance Dogs International (ADI) and the International Guide Dog Federation (IGDF), and from non-accredited U.S. assistance dog training facilities, on the numbers and types of dogs they placed in 2013 and 2014 with persons who have disabilities. ADI categories of assistance dogs are for guide, hearing, and service (including for assistance with mobility, autism, psychiatric, diabetes, seizure disabilities). Accredited facilities in 28 states and 3 provinces responded; accredited non-responding facilities were in 22 states and 1 province (some in states/provinces with responding accredited facilities). Non-accredited facilities in 16 states responded. U.S./Canada responding accredited facilities (55 of 96: 57%) placed 2,374 dogs; non-accredited U.S. facilities (22 of 133: 16.5%) placed 797 dogs. Accredited facilities placed similar numbers of dogs for guiding (n = 918) or mobility (n = 943), but many more facilities placed mobility service dogs than guide dogs. Autism service dogs were third most for accredited (n = 205 placements) and U.S. non-accredited (n = 72) facilities. Psychiatric service dogs were fourth most common in accredited placements (n = 119) and accounted for most placements (n = 526) in non-accredited facilities. Other accredited placements were for: hearing (n = 109); diabetic alert (n = 69), and seizure response (n = 11). Responding non-accredited facilities placed 17 hearing dogs, 30 diabetic alert dogs, and 18 seizure response dogs. Non-accredited facilities placed many dogs for psychiatric assistance, often for veterans, but ADI accreditation is required for veterans to have financial reimbursement. Twenty states and several provinces had no responding facilities; 17 of these states had no accredited facilities. In regions lacking facilities, some people with disabilities may find it inconvenient living far from any supportive facility, even if travel costs are provided. Despite accelerated U.S./Canada placements, access to well-trained assistance dogs continues to be limited and inconvenient for many people with disabilities, and the numerous sources of expensive, poorly trained dogs add confusion for potential handlers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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 teacher head, 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".