Veterinarians’ use of shared decision making during on‐farm interactions with dairy and beef producers
Bibliographic record
Abstract
Abstract Background The objectives of this study were to explore the level of shared decision making (SDM) between veterinarians and dairy and beef producers during on‐farm interactions and to identify factors associated with veterinarians’ use of SDM behaviours. Methods A cross‐sectional sample of food‐animal veterinarians and their clients were recruited in Ontario, Canada. Their on‐farm interactions were audio–video recorded. The recordings were analysed using the ‘Observing Patient Involvement in Decision Making’ (observer OPTION 5 ) instrument to determine the level of SDM utilised during preference‐sensitive decisions. A logistic regression model was developed to assess factors associated with a preference‐sensitive decision occurring. Meanwhile, a linear regression model was developed to identify factors associated with the level of SDM used. Results Forty‐one veterinarians participated, and 186 unique veterinarian–producer interactions were audio–video recorded and OPTION 5 score was calculated. SDM scores were low and comparable to other studies using the OPTION 5 instrument. The only factor associated with whether a preference‐sensitive decision occurred was the length of the veterinarian and producer's relationship (in years). As the length of their relationship increased, a preference‐sensitive decision was less likely to occur. The use of SDM behaviours was found to decrease as veterinarian burnout score increased. These findings demonstrate that SDM behaviours are being used by food‐animal veterinarians, yet an opportunity exists to further implement more producer‐centred SDM skills into on‐farm interactions. Limitations Small portions of veterinarian–producer conversation occurred outside of audio–video‐recorded interactions and were not included in the analysis. Conclusion The results of this study aid in further understanding on‐farm interactions between veterinarians and producers and can help to further improve veterinary communication curricula.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".