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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".