Initial Psychometric Evaluation of a Service Dog Attraction Assessment Grid for Youth With ASD and Related Disorders
Bibliographic record
Abstract
Animal-assisted interventions, such as the use of service dogs, represent an increasingly popular therapeutic avenue for youths with autism spectrum disorder (ASD). Before engaging anyone in this type of intervention, it could be justified to assess their level of attraction to animals. However, this aspect is rarely documented, and no existing instrument suits the specific challenges of ASD clients and the reality of the clinical setting. This paper describes the use and preliminary validation of an observation-based assessment tool developed by the Mira Foundation to evaluate the level of attraction toward dogs. The 9-item scale has been used by the team throughout the evaluation process of 1,010 potential candidates for a service dog. It is designed to describe and quantify observed behaviors during a standardized encounter with a non-familiar dog. The participants were aged between 2 and 26 years old (mean age = 8.13, SD = 4.3 years) and 87% had an ASD diagnosis. A total of 323 participants were assessed by a second rater, which allowed for interrater reliability analyses on each item of the scale. The factorial structure of the scale was determined by applying an exploratory factor analysis (EFA) on one-half of the sample, followed by a cross-validation study consisting of a confirmatory factor analysis (CFA) on the other half. Cronbach’s alpha was used to assess internal consistency. Based on weighted kappa statistics, interrater reliability showed strong agreement for individual items (Kw between 0.66 and 0.85). A two-factor solution was produced with EFA and confirmed through CFA. A Cronbach’s alpha of 0.89 when items from the first factor (attitude) were combined and 0.92 for the second factor (interaction) indicated good internal consistency for the identified subscales. These findings support the reliability of this assessment tool and its potential use in clinical and research domains.
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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 | 0.013 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".