Perceptions of laboratory animal veterinarians regarding institutional transparency
Bibliographic record
Abstract
Institutions using animals for research typically have a veterinarian who is responsible for the veterinary care programme and compliance with regulatory obligations. These veterinarians operate at the interface between the institution's animal research programme and senior management. Veterinarians have strong public trust and are well positioned to share information about animals used for scientific purposes, but their perspectives on sharing information with the public are not well documented and their perceptions of transparency may influence how institutional policies are developed and applied. The objective of our study was to analyse the perceptions of institutional transparency among laboratory animal veterinarians working at different universities. Semi-structured, open-ended interviews were used to describe perceptions of 16 attending veterinarians relating to animal research transparency. Three themes were drawn from the interviews: (i) reflections on transparency; (ii) reflections on culture; and (iii) reflections on self. Veterinarians reflected on their personal priorities regarding transparency and when combined with barriers to change within the institutions, sometimes resulted in reported inaction. For example, sometimes veterinarians chose not to pursue available opportunities for change at seemingly willing universities, while others had their initiatives for change blocked by more senior administrators. The sharing of information regarding the animals used for scientific purposes varied in how it was conceptualised by attending veterinarians: (i) true transparency; communication of information for the sake of openness; (ii) strategic transparency; attempt to educate people about animal research because then they will support it; (iii) agenda-driven transparency; selective release of positive stories to direct public opinion; and (iv) fearful non-transparency; not communicating any information for fear of negative opposition to animal research. Transparency was not perceived as an institutional priority by many of the veterinarians and a cohesive action plan to increase transparency that involves multiple universities was identified as a promising avenue to overcome existing barriers.
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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 | 0.033 | 0.069 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| 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".