Action cameras and the Roter interaction analysis system to assess veterinarian‐producer interactions in a dairy setting
Bibliographic record
Abstract
Herd health and production management (HH&PM) are critical aspects of production animal veterinary practice; therefore, dairy veterinarians need to effectively deliver these services. However, limited research that can inform veterinary education has been conducted to characterise these farm visits. The aim of the present study was to assess the applicability of action cameras (eg, GoPro cameras) worn by veterinarians to provide on-farm recordings, and the suitability of these recordings for comprehensive communication analyses. Seven veterinarians each recorded three dairy HH&PM visits. Recordings were analysed using the Roter interaction analysis system (RIAS), which has been used to evaluate medical conversations in human and companion animal contexts, and provided insights regarding the importance of effective clinical communication. However, the RIAS has never been used in a production animal environment. Results of this pilot study indicate that on-farm recordings were suitable for RIAS coding. Dairy practitioners use a substantial amount of talk allocated to relationship-building and farmer education but that communication patterns of the same veterinarian vary considerably between farm visits. Consecutive studies using this method will provide observational data for research purposes and promise to aid in the improvement of veterinary education through identification of communication priorities and gaps in dairy advisory discussions.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 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) | 0.000 | 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".