Using focus groups with dairy cattle veterinarians to explore learning about calf welfare
Bibliographic record
Abstract
Dairy calf welfare is a growing interest within the veterinary field. However, a limited understanding of the conception of calf welfare by dairy cattle veterinarians can hinder efforts to promote welfare improvements on farms. The aim of this study was to explore how focus groups can promote learning about dairy calf welfare issues among cattle veterinarians. Focus groups (n = 5), that collectively had 33 participants representing five Canadian provinces and different geographical regions, were conducted as part of a continuing education workshop for Canadian cattle veterinarians. Two trained individuals undertook exploratory data analyses using applied thematic analysis and adult learning theory to develop a codebook of the data and identify the main themes. There were three main themes about learning that emerged from guided peer-discussion: (i) defining a shared concept of animal welfare from the veterinary perspective to diagnose the problem; (ii) understanding the problems of calf welfare by self-examination and group reflection; and (iii) negotiating the best approach to address the problems through sharing of ideas on improving calf welfare, including strategies for addressing welfare problems. In conclusion, focus groups can facilitate animal welfare learning within the veterinary profession.
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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 | 0.031 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".