A focus group study of Ontario dairy producer perspectives on neonatal care of male and female calves
Bibliographic record
Abstract
Providing optimal calf care remains a challenge on many dairy farms and has important implications for the future health, welfare, and productivity of male and female calves. Recent research suggests that male dairy calves receive a lower quality of care early in life than female calves, but further investigation is required to determine the factors that influence this disparity. The objectives of this study were to understand dairy producer perspectives on neonatal calf care practices and explore differences between male and female calf care. Overall, 23 dairy producers in Ontario, Canada, participated in 4 focus groups about calf care practices that were recorded and evaluated qualitatively using thematic analysis. Major barriers for good calf care included lack of knowledge about the best management practices for calf care and the prioritization of farm resources toward the milking herd. Some producers also noted that farm infrastructure (particularly during challenging weather) and employee training were important limitations. The economic cost of providing good neonatal calf care was important primarily for male calves and acted as a motivation or a barrier depending on the producer's beliefs about calf care and how they chose to market their calves. The primary source of knowledge producers used to develop calf care practices was their own experience, although many also relied on dairy-industry advisors, most often veterinarians. Producers were motivated by social norms, along with intrinsic pride and obligation to provide good calf care, and these motives were influenced by their emotional state. Producers expressed beliefs about which aspects of calf care are most important-notably colostrum management-and appreciated simple and economical solutions to calf-rearing challenges. Calf care practices were varied, and we identified a diversity of knowledge, motivations, and barriers to adopting best management practices, which sometimes differed between male and female calves. Some producers said that they did not know what happened to their male calves after they left the farm and tended to prioritize the care of female over male calves in subtle ways, such as less timely provision of colostrum. The infrastructure investment and other costs associated with caring for male calves often limited their care, but producers were still motivated to provide adequate care for male calves. These findings represent potential targets for additional research and intervention strategies to improve calf care practices on dairy farms.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".