“It’s Like Having a Map”: An Exploration of Participating Pet Owners’ Expectations of Using Telemedicine to Access Emergency Veterinary Care
Bibliographic record
Abstract
(1) Background: Telemedicine is increasingly recognized as a potential way to help overcome barriers to accessing veterinary care. This study explored pet owners' perspectives on telemedicine services in situation they viewed as veterinary emergencies, focusing on both their access-to-care challenges and their expectations of such services. (2) Methods: Semi-structured, one-on-one interviews were conducted with 18 pet owners in Ontario, Canada. All participants were unable to access in-person care, leading them to a veterinary telemedicine service. Interviews were supplemented by an online questionnaire to collect demographic information. Thematic analysis was conducted on all interview transcripts. (3) Results: Participants' expectations of telemedicine were largely informed by their uncertainty about their pets' health in emergency situations. Anxiety was common, leading them to seek telemedicine for reassurance. Participants expected advice and guidance, viewing the process as collaborative. While acknowledging the benefits of telemedicine in their situation, participants also acknowledged the inherent limitations of this type of service, articulating a general preference for in-person veterinary care. (4) Conclusions: When in-person care is unavailable, telemedicine can offer pet owners valuable guidance, clarity, and comfort, although many pet owners may continue to prefer face-to-face consultations for comprehensive care.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".