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Record W2100534959 · doi:10.2460/javma.241.1.81

Gender differences in veterinarian-client-patient communication in companion animal practice

2012· article· en· W2100534959 on OpenAlexaffabout
Jane R. Shaw, Brenda N. Bonnett, Debra Roter, Cindy L. Adams, Susan Larson

Bibliographic record

VenueJournal of the American Veterinary Medical Association · 2012
Typearticle
Languageen
FieldHealth Professions
TopicVeterinary Practice and Education Studies
Canadian institutionsUniversity of CalgaryUniversity of Guelph
Fundersnot available
KeywordsContext (archaeology)Descriptive statisticsFamily medicineHUBzeroMedicineCompanion animalSample (material)PsychologyAnimal welfareNursingPet therapyVeterinary medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe the relationship between veterinarian and client genders and veterinarian-client-patient communication. DESIGN: Cross-sectional descriptive study. SAMPLE: Random sample of 50 companion animal practitioners in southern Ontario and a convenience sample of 300 clients and their pets. PROCEDURES: For each practitioner, 6 clinical appointments were videotaped, and the resulting 300 videotapes were analyzed with the Roter interaction analysis system (RIAS). Linear regression was conducted to study the relationship between demographic factors, measures of veterinarian-client-patient communication, and gender of the veterinarian and client. RESULTS: Female veterinarians conducted more relationship-centered appointments, provided more positive and rapport-building statements, talked more to the patient, and were perceived as less hurried or rushed, compared with male veterinarians. Clients were more likely to provide lifestyle-social information to female veterinarians. Same-gender veterinarian-client interactions were relationship centered and included client provision of more lifestyle-social information. CONCLUSIONS AND CLINICAL RELEVANCE: Gender influenced veterinarian-client-patient communication, and previously described physician gender differences in medical communication were largely replicated in the veterinary context.

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)0.005
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.687

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.254
GPT teacher head0.488
Teacher spread0.234 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations44
Published2012
Admission routes2
Has abstractyes

Explore more

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