Stakeholder views on treating pain due to dehorning dairy calves
Bibliographic record
Abstract
Abstract A common and painful management practice undertaken on most dairy farms is dehorning young calves (also called ‘disbudding’ when done on calves less than about two months of age). Despite much evidence the practice is painful, and effective means available to mitigate this pain, it is frequently performed without pain relief. The overall aim of this study was to describe different stakeholder views on the use of pain mitigation for disbudding and dehorning. Using an interactive, online platform, we asked participants whether or not they believed that calves should be disbudded and dehorned with pain relief and to provide reasons to support their choice. Participant composition was as follows: dairy producer or other farm worker (10%); veterinarian or other professional working with the dairy industry (7%); student, teacher or researcher (16%); animal advocate (9%); and no involvement with the dairy industry (57%). Of 354 participants, 90% thought pain relief should be provided when disbudding and dehorning. This support was consistent across all demographic categories suggesting the industry practice of disbudding and dehorning without pain control is not consistent with normative beliefs. The most common themes in participants’ comments were: pain intensity and duration, concerns about drug use, cost, ease and practicality and availability of alternatives. Some of the participants’ reasoning corresponded well with existing scientific evidence, but other reasons illustrated important misconceptions, indicating an urgent need for educational efforts targeted at dairy producers and dairy industry professionals advising these producers.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".