Disruptions in Transportation and Medical Care Experienced by Handlers of Assistance Dogs in Australia
Bibliographic record
Abstract
Anecdotal reports and limited available empirical evidence indicate that assistance-dog handlers are often denied access to places they are legally entitled to take their assistance dog. However, the frequency and contexts of access denials in Australia have not been established, and the emotional impacts of these denials are not well described. Furthermore, qualitative findings suggest that impromptu interactions with other people and dogs within the community can have both positive and negative impacts on the handler and assistance dog; larger-scale, quantitative research is needed. The aim of this study was to characterize the frequency and contexts, and emotional impacts, of assistance-dog access denials among handlers in Australia, as well as handler interactions with people and dogs. Handlers (n = 77) throughout Australia completed an online survey. Commercial passenger vehicles (CPVs, e.g., Uber/taxi) were the most commonly reported context for access denials, reportedly occurring about half the time, followed by hotels, restaurants, and cafés. Bystander support was rare in any setting. Some participants reported avoiding CPVs (52%), restaurants (13%), and medical/dental centers (13%) owing to prior access denials. The emotional impacts of the denials were very negative (e.g., annoyed, excluded, anxious, hurt). Having a visible or invisible disability had no bearing on the frequency of access denials, nor did having a conventional (e.g., Labrador Retriever) versus unconventional (e.g., Pug) breed of assistance dog. Unexpected interactions with people and other dogs were common; participants reported having a positive social interaction as a good outcome, and the dog becoming temporarily distracted as a common negative outcome. Unfortunately, eight participants (10%) had to retire a dog as an outcome of a negative interaction. Some free-text responses indicated that the reporting process for access denials is onerous and ineffective. Future research should seek to understand whether this can be remedied.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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 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".