Strengthening Workplace Well-Being in Research Animal Facilities
Bibliographic record
Abstract
In recent years, there has been an increased recognition of the potential cost of caring on the mental well-being of research animal facility personnel. While this issue is considered a normal consequence of caring for others, these stressors must be acknowledged and managed to ensure that the workplace culture remains positive and that employees are engaged. Factors that can contribute to these feelings in those working with animals in research include compassion and moral stress, issues related to staffing and scheduling of work, insufficient communication in the workplace, and public ambivalence toward the use of animals in science. The first step in developing a program is to survey facility personnel about their concerns, either formally (e.g., using a needs analysis) or informally. Two examples are provided to demonstrate different institutional approaches to assessing personnel needs and developing an internal compassion-resiliency program. The best programs are based on the needs and wants of personnel and these can be cost effective and geared at a grassroots level. Social support in the workplace, for example, through peer counseling, can be a highly effective means of helping personnel to build compassion-resiliency. Addressing mental well-being of research animal facility personnel is an important component of ensuring a positive culture of care in the workplace.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".