Attitudes toward the use of Animals in Chronic <i>versus</i> Acute Pain Research: Results of a Web-based Forum
Bibliographic record
Abstract
When asked about the use of animals in biomedical research, people often state that the research is only acceptable if pain and distress are minimised. However, pain is caused when the aim is to study pain itself, resulting in unalleviated pain for many of the animals involved. Consequently, the use of animals in pain research is often considered contentious. To date, no research has explored people's views toward different types of animal-based pain research (e.g. chronic or acute pain). This study used a web-based survey to explore people's willingness to support the use of mice in chronic versus acute pain research. The majority of the participants opposed the use of mice for either chronic (68.3%) or acute (63.1%) pain research. There was no difference in the levels of support or opposition for chronic versus acute pain research. Unsupportive participants justified their opposition by focusing on the perceived lack of scientific merit, or the existence of non-animal alternatives. Supporters emphasised the potential benefits that could arise, with some stating that the benefits outweigh the costs. The majority of the participants were opposed to pain research involving mice, regardless of the nature and duration of the pain inflicted, or the perceived benefit of the research. A better understanding of public views toward animal use in pain research may provide a stronger foundation for the development of policy governing the use of animals in research where animals are likely to experience unalleviated pain.
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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 | 0.024 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".