Individual drivers and barriers to adoption of disease control and welfare practices in dairy and beef cattle production: a scoping review
Bibliographic record
Abstract
The implementation of disease control and welfare practices is an essential part of limiting disease exposure in livestock, however successful adoption of these practices seem to be low in both the beef and dairy cattle industries. The main objectives of this scoping review were to characterize literature published exploring beef and dairy cattle producers' perceptions on the implementation of various disease control and welfare practices, identify major themes of drivers and barriers that influence producers' adoption of these practices, and identify current gaps in knowledge. A total of 2,486 articles were obtained from two database literature searches and screened, from which 48 articles published between 2010 and 2021 were deemed eligible and charted. Europe was the most common region for articles (58%). A majority of articles focused solely on dairy producers (52%). A wide range of barriers and drivers which were categorized into 4 and 5 key themes, respectively. The most commonly mentioned driver was animal health, welfare, and safety, while the most common barrier was costs. Potential gaps in literature were identified, including the underrepresentation of beef producer perceptions relative to dairy producers in current literature.
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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 | 0.010 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".