Drug use stigma, accidental pet poisonings, and veterinary care: results from a survey of pet owners in Vancouver, British Columbia
Bibliographic record
Abstract
Introduction The effect of drug use on physical health, mental wellbeing, and quality of life can extend to family members, including children, and even companion animals. People who use drugs, a medically vulnerable population, face stigma and shaming when accessing healthcare services that engender mistrust and hinder future access. Yet, in an emergency where a pet has accidentally ingested drugs, there is no research on whether this stigma may prevent owners from seeking veterinary help. The objectives of this study were to describe actions taken by pet owners after accidental pet drug poisonings in Vancouver, British Columbia and understand how drug use-related stigma is associated with owners’ decision to seek veterinary care. Methods We surveyed two populations of pet owners, a general population recruited online (n = 82) and a sample recruited in-person at two outreach services that assist low-income pet owners in the Downtown Eastside of Vancouver (n = 32). Participants who had not experienced a pet poisoning were asked about their actions and attitudes in a hypothetical drug poisoning event. Results Within the general population sample, 64 (78%) responded based on a hypothetical scenario, and in this group, the concern that a veterinarian might remove their animal was associated with higher self-reported discrimination in three domains: general discrimination, discrimination accessing non-health services, and discrimination accessing health-related services. A higher perceived experience of discrimination within each category was also associated with a greater concern that veterinary staff might report them to the authorities or social services, as well as an increased expectation of hiding information from a veterinarian, including information about what drug the animal ingested and how the animal was exposed. Those who had experienced a poisoning emergency were asked about their actions and encounters in the most recent poisoning. More than half sought veterinary assistance. Among those that did not, some reasons owners avoided veterinary care included confidence in their ability to treat the animal at home (n = 5), or fear of discrimination (n = 1) or punishment (n = 1). Discussion Our findings suggest that drug use-related stigma may contribute to a hesitancy to seek veterinary care or fully disclose information in an accidental pet poisoning.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".