Using Stakeholder Focus Groups to Refine the Care of Pigs Used in Research
Bibliographic record
Abstract
Research organizations should be proactive in regularly evaluating and refining their animal care and use programs in order to advance animal welfare and minimize distress. Pigs are often used in research, but few empirical studies have examined optimal husbandry and research use practices for pigs in a research environment. We developed the Pig Welfare Working Group (PWWG) to address the need for more formal guidelines on the management and use of pigs in research. The PWWG was a stakeholder focus group whose goal was to identify challenges and opportunities relevant to improving animal welfare through collaboration, knowledge sharing, and inclusive decision-making. Through consensus building, the PWWG developed 12 recommendations for behavioral management, housing, research procedures, transportation, and rehoming programs. The recommendations were rolled out across the contract research organization, business units, sites, and countries. Follow up will be conducted regularly to assess welfare, monitor progress toward implementing the recommendations, and recognize and reward participants making changes at their site.
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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 | 0.204 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.010 | 0.003 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".