Veterinarians’ use of the Talking Physical Exam as a communication tool
Bibliographic record
Abstract
OBJECTIVE: To describe veterinarians' communication of the companion animal physical exam (CAPE) to veterinary clients and to identify factors associated with the number of physical exam components communicated by veterinarians to clients. SAMPLE: 376 video-recorded veterinarian-client-patient interactions, involving 60 veterinarians. PROCEDURES: 18 CAPE components were studied in relation to veterinarians' use of 7 communication-related parameters. A mixed linear regression model was used to assess veterinarian, patient, and appointment factors associated with the number of components conveyed by a veterinarian. RESULTS: Veterinarians conveyed 1,566 of 2,794 (56.1%) of the components that they examined to clients, as having been examined. Of those components that were examined and conveyed by veterinarians, the impact of the finding was communicated for 496 of 1,566 (31.7%) of the components. Visual aids and take-home literature were each used in relation to an examined component during 15 of the 376 interactions (4%). A significant association was found between number of CAPE components conveyed and gender of the veterinarian (females conveyed 1.31 more), as well as the type of appointment (2.57 more were conveyed in wellness appointments and 1.37 more in problem appointments, compared to rechecks). CLINICAL RELEVANCE: Findings identify an opportunity for veterinarians to further emphasize components of the CAPE, which may in turn increase clients' perceived value of the CAPE due to understanding the benefits for their pet. This may be accomplished with the Talking Physical Exam, in which veterinarians discuss CAPE components findings with clients in real time, and the relevance of the findings to the patient's health.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".