Compassion Fatigue and Coping Mechanisms of Laboratory Animal Professionals from Europe, China, and Japan
Bibliographic record
Abstract
Laboratory animal professionals (LAP) may experience situations that contribute to compassion fatigue (CF). The goal of this research was to better understand CF in LAP in and across employment categories. Surveys were distributed through LAP organizational listservs in the European Union (EU), China, and Japan, and results were analyzed to identify CF prevalence, personal and work-related factors, coping mechanisms, and beneficial work-support programs. Independent χ2-tests compared personal and work-related factors and feelings of CF. Feelings of CF and coping mechanisms were compared with personality scores using independent sample t tests. There were 302 respondents from the EU, 39 from China, and 77 from Japan. Over half of respondents from the EU (52%) and China (56%) reported experiencing CF (52%), with fewer (32%) reporting CF in Japan. No major differences were found based on employer type. Personality scores were significantly related to feelings of CF and preferred coping mechanisms. Work-related factors that contributed to feelings of CF in over half of respondents included staffing levels, workplace relationships, and availability of programs geared to address CF. Across regions, talking to someone, physical activity, getting away from work, and self-care were effective coping mechanisms in over 50% of respondents. Fewer than 30% of respondents indicated that their place of employment had CF support programs, and even fewer (8% to 28%) indicated that these programs were helpful. The study results suggest that to be effective, employer CF programs for LAP should consider providing quiet places at work and programs for self-care, promoting physical and mental health and social support systems, and establishing opportunities to memorialize animals.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".