Laboratory Animal Welfare Meets Human Welfare: A Cross-Sectional Study of Professional Quality of Life, Including Compassion Fatigue in Laboratory Animal Personnel
Bibliographic record
Abstract
Laboratory animal personnel may experience significant stress from working with animals in scientific research. Workplace stress can be assessed by evaluating professional quality of life, which is comprised of compassion fatigue (i.e., burnout and secondary traumatic stress) and compassion satisfaction. This research aimed to explore the associations between risk factors and professional quality of life in laboratory animal personnel. In a cross-sectional, convenience sample design, laboratory animal personnel were recruited from widespread online promotion. A total of 801 personnel in the United States or Canada completed an online survey regarding professional quality of life, social support, euthanasia, enrichment, stress/pain levels, and human-animal interactions. Participants worked in a wide range of settings (e.g., industry, academia), research types (e.g., basic, applied, regulatory), species (e.g., non-human primates, mice), and roles (e.g. animal caretaker, veterinarian). Data were analyzed using general linear models. Personnel who reported higher compassion fatigue also reported lower social support, higher animal stress/pain, higher desire to implement more enrichment, and less control over performing euthanasia (p’s0.05). Our findings show that the professional quality of life of laboratory animal personnel is associated with several factors. Personnel reporting poorer professional quality of life also reported less social support, higher animal stress/pain, less enrichment diversity/frequency and wished they could provide more enrichment, using physical euthanasia, and less control over performing euthanasia. Poorer professional quality of life was also seen in personnel working as trainers, at universities, and longer hours. This study contributes important empirical data that may provide guidance for developing interventions (e.g., improved social support, decreased animal stress, increased animal enrichment diversity/frequency, greater control over euthanasia) to improve laboratory animal personnel’s professional quality of life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".