Using quantitative methods to understand how dairy farmer wellbeing connects to farm management, barn design, technology, and animal welfare
Bibliographic record
Abstract
The agricultural profession, particularly dairy farming, is known for its demanding nature, both physically and mentally. Dairy farmers encounter various challenges including long working hours, physical labour, financial pressures, and unpredictable weather conditions. My objective was to explore different farm management practices, farming factors, and the prevalence of mental and physical health issues among dairy farmers in Western Canada and Ontario. Statistical analyses included t-tests, ANOVA, and chi-squared tests to compare means with general population averages and to investigate associations between mental health scores and physical health outcomes with survey variables. The results of the survey indicated that farmers (n=115) scored significantly higher on perceived stress (P<0.001), anxiety (P<0.001), depression (P=0.04), and resilience scales (P<0.001) compared to the general population. The results also highlighted concerns regarding the physical health of dairy farmers, with the majority reporting work-related injuries and health issues. I identified significant differences when comparing mental and physical health with work-life balance, social environment and support, and specific dairy-related stressors. Farmers faced both personal and farming-related financial stressors. Surprisingly, health outcomes did not differ based on housing or milking system, management practices, farm responsibilities, or financial and transition planning variables. Therefore, dairy farmers appear to have similar well-being in different production systems, but farm finance, feed cost, weather, and workload constraints are major stressors. I then adopted a more holistic approach to cluster farmers based on their well-being and farm management. The analysis identified four distinct groups of dairy farmers based on their survey responses. It also emphasized the need for a deeper understanding of the unique challenges faced by individual farmers in their production systems. Without this understanding, there is a risk of developing intervention plans that are ineffective or inappropriate for the Canadian dairy industry. The typologies developed in this research offer a promising foundation for providing tailored support resources.
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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 | 0.019 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".