Connecting farmer mental health with cow health and welfare on dairy farms using robotic milking systems
Bibliographic record
Abstract
Abstract The objective of this exploratory, preliminary study was to survey dairy farmers using robotic milking systems to better understand their mental health and potential connections to their cow health and welfare. Only farms using robotic milking systems in Ontario, Canada were visited for collection of data on management practices, cow welfare, and milk production and quality. Those farmers also completed an online survey that included validated psychometric scales used to assess resilience, stress, anxiety, and depression; results from 28 farms were analysed. Thirty cows per farm (or 30% for herds > 100 milking cows) were scored for body condition (five-point scale: 1 = thin to 5 = over-conditioned) and lameness (five-point scale: 1 = sound to 5 = lame); cows with a Body Condition Score ≤ 2.5 and lameness score ≥ 4 were defined as under-conditioned and severely lame, respectively. Farmer stress was positively associated with severe lameness prevalence, was greater for females vs males, and was greater for those feeding manually vs using an automated feeder. Anxiety and depression were greater for females vs males, and for those working alone, feeding manually, and with lesser milk protein percentage. Anxiety was also positively associated with the prevalence of severe lameness. Resilience was greater for those with automated feeding systems, but tended to be negatively associated with milk yield per robot and positively associated with milk somatic cell count. This is the first study to identify associations between farmer well-being and cow lameness, udder health, and milk yield. With future research, we can better understand this relationship to improve the well-being of both agricultural animals and their caretakers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".