A Survey to Understand Public Opinion regarding Animal use in Medical Training
Bibliographic record
Abstract
A random survey was performed by ORC International Telephone CARAVAN®, on 24-27 March 2016, by trained interviewers. The aim of this survey was to gain further understanding of public perceptions in the United States of laboratory animal use, specifically for the purposes of medical training. Five statements were read in random order to the participants, who were then asked whether they agreed or disagreed with the statement. Survey responses were obtained from 1011 participants. For the combined statements: "If effective non-animal methods are available to train a) medical students and physicians, b) emergency physicians and paramedics, and c) paediatricians, those methods should be used instead of live animals", most respondents (82-83%) agreed. For the statement: "You want your doctor to be trained by using methods that replicate human anatomy instead of live animals", most respondents (84%) agreed. For the statement: "If effective non-animal methods are available, it is morally wrong or unethical to use live animals to train medical students, physicians and paramedics", 67% of respondents agreed. Responses were similar among the 15 pre-specified demographic subgroups. Given that effective non-animal training methods are readily available, the survey suggests that a substantial majority of the public wants the use of animals in medical training to cease.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".