Does Benchmarking of Rating Scales Improve Ratings of Search Performance Given by Specialist Search Dog Handlers?

An investigation into how accurately UK rabbit owners identify pain in their pet rabbits

BACKGROUND

Rabbits are popular family pets. They are prey species and so have evolved to hide signs of illness and pain. Recent research has developed robust pain scales for use in a clinical setting, but to date none has examined rabbit owners' ability to recognise pain in their animals. This study investigated how owners identify pain in their pet rabbits and their ability to correctly identify different levels of pain, in order to determine any need for owner education in this area.

METHODS

Owners were recruited via Facebook and a two-part online survey was distributed. Part one collected data on demographics, owners' knowledge of pain signs and beliefs about pain in rabbits. Part two asked respondents to pain score eight videos of rabbits in different levels of pain for comparison to pain scores made by three experts. We used a simplified version of the Bristol Rabbit Pain Score (BRPS) which involved a single 0 to 3 scale. We explored the number of pain signs each respondent could list, the total score given to the videos, and their deviation from the experts' scores.

RESULTS

A total of 500 respondents completed part one of the survey and 345 additionally completed Part two. Respondents were on average able to state five signs of pain (range 0-12), but females stated significantly more (p = 0.018), as did those who worked with rabbits (p = 0.004) and those with experience of their rabbit having an operation (p = 0.01). Overall, 98.6% of respondents thought rabbits felt pain as much or more that dogs and cats. In Part two, respondents more frequently agreed with the experts when identifying rabbits in no pain (88.8%) and severe pain (65.2%), but there was lower agreement when identifying mild (28.4%) and moderate pain (43.2%). Respondents overall rated pain lower than experts with an average total pain score of 11.9 compared to 18 given by the experts.

CONCLUSIONS

Most rabbit owners are able to list numerous pain signs and are generally able to identify pain-free rabbits and those in severe pain. Owners' ability to differentiate between mild and moderate pain is more limited and could benefit from training in the subtler signs of pain. Veterinary professionals are well placed to educate owners about signs of pain in rabbits and should be aware of areas where owners' knowledge can be improved.

Designing criterion measures for physically demanding jobs

The Bureau of Labor Statistics reported that 39.1% of the civilian workforce in the United States performs physically demanding jobs that require lifting, carrying, pushing/pulling, kneeling, stooping, crawling, and climbing activities in varied environmental conditions. United States military occupations are similar to those in the civilian sector involving equipment installation, emergency rescues, and maintenance, along with combat arms occupations. This article provides an overview of the types of criterion measures used to assess the physical domain and approaches for designing and evaluating the criteria. It also includes a method for generating criterion measures that are applicable across multiple jobs.

Phenotypic correlates of the working dog microbiome

Dogs have a key role in law enforcement and military work, and research with the goal of improving working dog performance is ongoing. While there have been intriguing studies from lab animal models showing a potential connection between the gut microbiome and behavior or mental health there is a dearth of studies investigating the microbiome-behavior relationship in working dogs. The overall objective of this study was to characterize the microbiota of working dogs and to determine if the composition of the microbiota is associated with behavioral and performance outcomes. Freshly passed stools from each working canine (Total n = 134) were collected and subject to shotgun metagenomic sequencing using Illumina technology. Behavior, performance, and demographic metadata were collected. Descriptive statistics and prediction models of behavioral/phenotypic outcomes using gradient boosting classification based on Xgboost were used to study associations between the microbiome and outcomes. Regarding machine learning methodology, only microbiome features were used for training and predictors were estimated in cross-validation. Microbiome markers were statistically associated with motivation, aggression, cowardice/hesitation, sociability, obedience to one trainer vs many, and body condition score (BCS). When prediction models were developed based on machine learning, moderate predictive power was observed for motivation, sociability, and gastrointestinal issues. Findings from this study suggest potential gut microbiome markers of performance and could potentially advance care for working canines.

Development of a Performance Monitoring Instrument for Rating Explosives Search Dog Performance

The growing body of working dog literature includes many examples of scales robustly developed to measure aspects of dog behavior. However, when comparing behavior to working dog ability, most studies rely on training organizations' own long-established ratings of performance, or simply pass/fail at selection or certification as measures of success. Working ability is multifaceted, and it is likely that different aspects of ability are differentially affected by external factors. In order to understand how specific aspects of selection, training, and operations influence a dog's working ability, numerous facets of performance should be considered. An accurate and validated method for quantifying multiple aspects of performance is therefore required. Here, we describe the first stages of formulating a meaningful performance measurement tool for two types of working search dogs. The systematic methodology used was: (1) interviews and workshops with a representative cross-section of stakeholders to produce a shortlist of behaviors integral to current operational performance of vehicle (VS) and high assurance (HAS) search dogs; (2) assessing the reliability and construct validity of the shortlisted behavioral measures (at the behavior and the individual rater level) using ratings of diverse videoed searches by experienced personnel; and (3) selecting the most essential and meaningful behaviors based on their reliability/validity and importance. The resulting performance measurement tool was composed of 12 shortlisted behaviors, most of which proved reliable and valid when assessed by a group of raters. At the individual rater level, however, there was variability between raters in the ability to use and interpret behavioral measures, in particular, more abstract behaviors such as Independence. This illustrates the importance of examining individual rater scores rather than extrapolating from group consensus (as is often done), especially when designing a tool that will ultimately be used by single raters. For ratings to be practically valuable, individual rater reliability needs to be improved, especially for behaviors deemed as essential (e.g., control and confidence). We suggest that the next steps are to investigate why individuals vary in their ratings and to undertake efforts to increase the likelihood that they reach a common conceptualization of each behavioral construct. Plausible approaches are improving the format in which behaviors are presented, e.g., by adding benchmarks and utilizing rater training.