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Record W4407255935 · doi:10.3390/vetsci12020136

Promoters and Detractors Identify Virtual Care as “Worlds Better than Nothing”: A Qualitative Study of Participating Veterinarians’ Perception of Virtual Care as a Tool for Providing Access

2025· article· en· W4407255935 on OpenAlexaboutno aff
Rosalie Fortin-Choquette, Jason B. Coe, C.A. Bauman, Lori M. Teller

Bibliographic record

VenueVeterinary Sciences · 2025
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNothingPerceptionQualitative researchInternet privacyPsychologyMedicineNursingData scienceComputer scienceSociologyEpistemologyAnthropologyNeurosciencePhilosophy

Abstract

fetched live from OpenAlex

(1) Background: Veterinary virtual care holds the potential to alleviate some barriers to accessing care, yet concerns within the profession exist. Understanding veterinarians' perspectives and identifying the potential opportunities and challenges that virtual care poses for access to veterinary care are thus needed. (2) Methods: Semi-structured interviews were conducted virtually with 22 companion-animal veterinarians practicing across Canada and the United States. Interviews were accompanied by an electronic survey, with which a Net Promoter Score (NPS) was calculated for each participant. Using their NPS, participants were categorized as a "promoter" or "detractor", with respect to their perspective on veterinary virtual care. A thematic analysis was conducted on verbatim transcripts of the interviews. (3) Results: A total of 11 detractors and 11 promoters were interviewed. Four subthemes were identified, including the following: (1) there are limitations to virtual care, (2) virtual care plays a role in access to care, (3) "virtual care is better than no care" and (4) virtual care offers specific value in supplementing in-person care. (4) Conclusion: When no other option for care delivery exists, virtual care was viewed as a way to increase access to veterinary care.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.315
GPT teacher head0.581
Teacher spread0.265 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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