How benchmarking motivates colostrum management practices on dairy farms: A realistic evaluation
Bibliographic record
Abstract
This study used realistic evaluation to determine how, and in which circumstances, providing dairy farmers with benchmarked data on their calves can motivate improved colostrum management practices. Dairy farmers from British Columbia, Canada, were recruited through 2 veterinary clinics that offered benchmarking of calf data as part of their services. For approximately 8 wk, blood samples were collected from newborn calves to evaluate serum total protein concentrations as an indicator of the effectiveness of the farmer's colostrum management. These data were analyzed separately for heifer calves ("replacement calves") and non-replacement calves, including males and beef crossbred females ("surplus calves"). The results of these analyses were benchmarked against other participating herds and presented to dairy farmers (n = 27) by their herd veterinarian (n = 7). Follow-up interviews were conducted separately with the farmers and veterinarians after each meeting to determine their perspectives on the utility of this benchmarking strategy. Therefore, a total of 42 interviews were coded, and realistic evaluation was used to determine common contexts and mechanisms that contributed to the success or failure of the benchmark meeting, with success characterized by farmers' expressed intention to improve their colostrum management practices. Four important contexts were identified that influenced the outcome of the benchmark meetings: (1) farm resources (e.g., facility limitations), (2) the farmer's perception of their calf performance, (3) management strategies, and (4) the farmer's personal values. Depending on these contexts, some farmers intended to improve their calf care practices based on resources the benchmark meeting provided, which included illustrative data and veterinary advice. These resources motivated change through influencing farmer decision-making, which depended on the value they saw in the data as a decision-making tool. The economic or moral interest farmers expressed in their surplus calves also influenced whether farmers intended to implement management changes. Recommendations for future implementation of benchmarking include targeting producers who are motivated to improve and who value the future performance of their calves, those who have engaged calf care personnel, and those who prefer data-driven decision-making. This study supports the important role veterinarians can play in motivating improved calf care practices through providing benchmarking services.
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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 | 0.052 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".