Abstract

Assistance dogs can greatly improve the lives of people with disabilities. However, a large proportion of dogs bred and trained for this purpose are deemed unable to successfully fulfill the behavioral demands of this role. Often, this determination is not finalized until weeks or even months into training, when the dog is close to 2 years old. Thus, there is an urgent need to develop objective selection protocols that can identify dogs most and least likely to succeed, from early in the training process. We assessed the predictive validity of two candidate measures employed by Canine Companions for Independence (CCI), a national assistance dog organization headquartered in Santa Rosa, CA. For more than a decade, CCI has collected data on their population using the Canine Behavioral Assessment and Research Questionnaire (C-BARQ) and a standardized temperament assessment known internally as the In-For-Training (IFT) test, which is conducted at the beginning of professional training. Data from both measures were divided into independent training and test datasets, with the training data used for variable selection and cross-validation. We developed three predictive models in which we predicted success or release from the training program using C-BARQ scores (N = 3,569), IFT scores (N = 5,967), and a combination of scores from both instruments (N = 2,990). All three final models performed significantly better than the null expectation when applied to the test data, with overall accuracies ranging from 64 to 68%. Model predictions were most accurate for dogs predicted to have the lowest probability of success (ranging from 85 to 92% accurate for dogs in the lowest 10% of predicted probabilities), and moderately accurate for identifying the dogs most likely to succeed (ranging from 62 to 72% for dogs in the top 10% of predicted probabilities). Combining C-BARQ and IFT predictors into a single model did not improve overall accuracy, although it did improve accuracy for dogs in the lowest 20% of predicted probabilities. Our results suggest that both types of assessments have the potential to be used as powerful screening tools, thereby allowing more efficient allocation of resources in assistance dog selection and training.

Highlights

  • Assistance dogs can greatly improve the lives of people with disabilities

  • Because Experiments 1–2 suggested that the C-BARQ and IFT were both useful measures for predicting training outcomes, in Experiment 3 we investigated whether predictive accuracy could be improved by combining data from both instruments

  • Several previous studies have identified associations between behavioral or temperamental variables and working dog outcomes, few studies have moved beyond association to formal prediction of outcomes with an independent sample

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Summary

Introduction

Assistance dogs can greatly improve the lives of people with disabilities. By performing tasks such as picking up dropped items, opening doors, and turning on and off lights, they allow their handlers to approach life with greater independence and confidence. At Canine Companions for Independence (CCI)— the largest nonprofit provider of assistance dogs for people with physical disabilities in the United States–the success rate over the past 13 years has averaged 43% when breeders and medical releases are excluded To be successful, these dogs must be robust to environmental stressors (large crowds, loud noises) and distractions (other animals and people, food on the ground), and exhibit impulse control, flexible and sustained attention, appropriate social behavior, and independent problem solving. Given the extensive resources required to raise and train these dogs, predicting the development and proficiency of these skills as early as possible is crucial to saving time and expense, while ensuring productive placements

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