Use of health parameter trends to communicate pet health information in companion animal practice: A mixed methods analysis
Bibliographic record
Abstract
BACKGROUND: Reviewing patient health parameter trends can strengthen veterinarian-client-patient relationships. The objective of this study is to identify characteristics associated with veterinarians' communication of health parameter trends to companion animal clients. METHODS: Using a sequential exploratory mixed methods design, independent pet owner (n = 27) and veterinarian (n = 24) focus groups were conducted and analysed via content analysis to assess perceptions of how health parameter trends are communicated by veterinarians. Subsequently, a quantitative assessment of video recorded veterinary appointments (n = 917) compared characteristics identified in focus groups with health parameter trend discussions in practice. A mixed logistic model was used to assess characteristics associated with the occurrence of weight trend discussions. RESULTS: Fifteen characteristics relating to veterinarians' use of health parameter trends were identified across focus groups. Veterinarians discussed 77 health parameter trends in relation to bodyweight (57/77), blood work (15/77) and other health parameters (5/77), within 73 (73/917) appointments. The odds of a weight trend discussion were higher if the veterinarian identified the pet as overweight or obese compared to an ideal bodyweight (odds ratio (OR) = 2.17; 95% confidence interval (CI) = 1.15-4.09; p = 0.016). CONCLUSION: Mention of a health parameter trend was uncommon and rarely included use of visual aids. Health parameter trends related to bodyweight were discussed reactively, rather than proactively.
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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 | 0.049 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".