Use of the measure of patient-centered communication to analyze euthanasia discussions in companion animal practice
Bibliographic record
Abstract
OBJECTIVE: To characterize veterinarian-client communication with undisclosed standardized clients (USCs) during discussions regarding euthanasia of a pet. DESIGN: Descriptive study. SAMPLE POPULATION: 32 companion animal veterinarians (16 males and 16 females) in southern Ontario. PROCEDURES: During 2 clinic visits, 2 cases (a geriatric dog with worsening arthritis and a cat with inappropriate urination) designed to stimulate discussion regarding euthanasia of a pet were presented by different USCs (individuals trained to consistently present a particular case to veterinarians without disclosing their identity). Discussions were audio recorded and analyzed by use of the measure of patient-centered communication (MPCC [a tool to assess and score physician communication behaviors]). Veterinarian and client statements were classified by means of 3 patient-centered components: exploring both the disease and the illness experience, understanding the whole person, and finding common ground. RESULTS: 60 usable recorded discussions were obtained (31 veterinarians; 30 discussions/case). Overall, MPCC scores were significantly lower for the geriatric dog case. For both cases, veterinarians scored highest on finding common ground and lowest on exploring both the disease and the illness experience. Lack of exploration of client feelings, ideas, and expectations and the effect of the illness on the animal's function resulted in low scores among veterinarians. CONCLUSIONS AND CLINICAL RELEVANCE: Results indicated that the use of USCs and the MPCC are feasible methods for analysis of veterinarian-client communication during companion animal euthanasia discussions. Findings suggested that some veterinarians do not fully explore client concerns or facilitate client involvement in euthanasia decision making.
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 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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".