Trauma in Animal Protection and Welfare Work: The Potential of Trauma-Informed Practice
Bibliographic record
Abstract
Those who work in the animal protection and welfare (APW) sector are consistently exposed to human and animal suffering, particularly those who witness animal surrenders and seizures. Continued exposure to suffering can result in stress, anxiety, burnout, and compassion fatigue, which are detrimental to individual and organizational well-being. The aim of this study was to understand the challenges experienced by Canadian APW workers, and to explore how trauma-informed approaches can be implemented to help mitigate these challenges. To achieve this, we utilized purposive sampling to seek workers in the APW sector who had experience with animal surrender and/or seizure. Telephone interviews were conducted with 11 participants. Participants reported experiencing many challenges that negatively impacted their mental health; this article summarizes them by focusing on two key themes drawn from the narratives of the participants: feeling unprepared and forced strength. Trauma-informed practices are explored as a means to prevent compassion fatigue and burnout, and to increase the resilience of individuals and organizations. We suggest trauma-informed practices help APW workers manage job-related stressors while also providing a more compassionate experience for animal guardians. Further, we propose that trauma-informed practices are a crucial component in facilitating respectful relationships with the communities that APW organizations serve.
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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 | 0.035 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.027 | 0.087 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.005 | 0.010 |
| 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".