Dairy farmer advising in relation to the development of standard operating procedures
Bibliographic record
Abstract
Standard operating procedures (SOP) are increasingly required on farms participating in animal welfare assurance programs, such as the Dairy Farmers of Canada's proAction initiative and the National Dairy FARM Program in the United States. However, little is known about the use of SOP on farms and who is involved in their development. Literature from other industries shows the importance of including advisors when developing SOP. Despite veterinarians being viewed by many farmers as trusted sources of information, little is known about their involvement in SOP development. The aim of this study was to better understand: (1) what advice from researchers and veterinarians is considered when developing an SOP and (2) what factors affect advice adherence. Participants in this study were farmers (n = 9) from 6 dairy farms in the Fraser Valley region of British Columbia, Canada and their herd veterinarians (n = 5). Structured and semi-structured interviews and participant observation were undertaken from April to December 2018, and the resulting data were analyzed using thematic analysis. In relation to the first aim, we identified 3 main themes: (1) the purpose of the SOP, (2) developing an SOP, and (3) accountability and tracking of procedures. For the second aim, 5 themes emerged: (1) feasibility of the advice, (2) resources required, (3) priority of the advice, (4) other actors involved, and (5) the importance of data. Collectively, these findings suggest that a farm-specific SOP that actively tracks procedures is most beneficial, and that advice adherence is context dependent.
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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 | 0.027 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".