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 member and candidate facilities 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 mobility, autism, psychiatric, diabetes, seizure) dogs. Accredited facilities in 22 states and 3 provinces responded; other accredited facilities in 22 states and 1 province (sometimes in the same states/provinces as accredited facilities) did not respond. Non-accredited facilities in 16 states responded. Results from 55 U.S./Canada responding accredited facilities show placements of 2,374 dogs; results from 22 non-accredited U.S. facilities show 797 placements. Accredited facilities placed similar numbers of dogs for guiding (n = 918) or mobility (n = 943). Mobility dogs were placed by more U.S./Canada facilities than were guide dogs; some of these facilities placed more dogs for autism or another role than for mobility. Autism dogs were third most for accredited (n = 205) and U.S. non-accredited (n = 72) facilities. Psychiatric dogs were fourth most common in accredited placements (n = 119), accounting for most placements (n = 526) in non-accredited facilities. Other U.S./Canada accredited placements were for: hearing (n=109); diabetic alert (n=69), and seizure alert (n=11). Responding non-accredited facilities placed 17 hearing dogs, 30 diabetic alert dogs, and 18 seizure alert/response dogs. Mainly non-accredited facilities placed dogs for psychiatric assistance, but veterans then are disqualified for financial reimbursement. Autism dogs were increasingly being placed even by accredited organizations. Twenty states and several provinces had no responding facilities; 17 of these states had no accredited facilities. In regions lacking facilities, people with disabilities face particular geographic challenges, living far from any supportive facility. Despite accelerated U.S./Canada placements, availability of well-trained assistance dogs continues to be challenging and inconvenient for many people with disabilities, and the numerous sources of expensive, poorly trained dogs can 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) | 0.000 | 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".