Use of nonaversive handling and training procedures for laboratory mice and rats: Attitudes of American and Canadian laboratory animal professionals
Bibliographic record
Abstract
Nonaversive or low stress handling techniques can reduce fear and stress in research rodents, ultimately improving study data quality. Uptake of low stress handling has been slow in the USA and Canada. In this study we explored the understanding, experience, and attitudes toward low stress handling of rats and mice in laboratory animal professionals from the USA (US) and Canada (CA). Participants (n = 40) were recruited for a standardized interview and job categories were divided into veterinary/PhD level roles (doctoral level; DL) and non-veterinary/non-PhD level roles (non-doctoral level, NDL) (US: 23, DL: 9, NDL: 14; CA: 17, DL: 8, and NDL: 9). Interviews were transcribed and analyzed using NVIVO. Two research assistants independently coded themes for each question and consolidated responses based on commonality. Laboratory animal professionals understood the benefits of low stress handling and training techniques with rats and mice, stating reduced stress, better data, and improved welfare, with CA participants more likely to mention animal welfare as a benefit, and DL more likely to mention improved research data and reduced stress. Participants across demographic groups indicated improved job satisfaction and decreased stress as the positive impacts low stress handling would have on their positions. The primary perceived barriers to low stress handling implementation were researcher attitudes, the time needed to implement and use these techniques, and training personnel to use the techniques properly and consistently. To promote refinement of handling of rats and mice, more educational opportunities on the benefits and implementation of low stress handling techniques need to be provided to laboratory animal professionals, as well as to researchers.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".