Connecting farmer well-being with cattle health and perceptions of wildlife on dairy and beef farms in Western Canada and Ontario
Bibliographic record
Abstract
This study surveyed 88 dairy and 17 beef farmers in Western Canada and Ontario to assess farmer well-being, and how it is connected to cattle health and perceptions of wildlife. Well-being was assessed using validated psychometric scales for mental health, sleep and injuries for physical health, and questions about social well-being. The survey additionally assessed farm management, animal health (including mastitis, and calf mortality), and perceptions and management of wildlife. Dairy farm visits (n=66) were conducted to assess lameness, body condition, and knee, neck, and hock lesions on a representative sample of each herd’s lactating cows (30% to a maximum of 69 cows). Dairy farmer responses were analyzed statistically, and beef farmer data are presented using descriptive statistics due to a low response rate. For dairy farmers, clinical lameness tended to be negatively associated with stress scores (P=0.07) and anxiety scores (P=0.06), and mastitis incidence was positively associated with stress scores (P=0.02). Beef farmers reported stress related to weather, pasture condition, and finances, but were generally satisfied with their personal relationships at home. Associations between beef farmers’ well-being and cattle health could not be drawn in this study. Overall, there was a connection between farmer well-being and animal health on dairy farms, but more research is needed to determine the factors that influence these associations. Regarding wildlife perceptions, both dairy and beef farmers viewed mice and rats, raccoons, and corvids negatively. Beef farmers additionally had negative perceptions of small mammals and large predatory mammals. Dairy farmers with negative perceptions of mice tended to have higher depression scores (P=0.0002), as well as lower resilience scores (P=0.07), even when considering region. The majority of dairy and beef farmers reported using wildlife control for mice, raccoons, and corvids and reported choosing these methods based on effectiveness, ease of use, and cost.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".