Record, Recall, Reflect: A Qualitative Examination of Compassion Fatigue in Toronto Zoo Staff
Bibliographic record
Abstract
This study explored Toronto Zoo animal-care professionals’ (ACPs) experiences with compassion fatigue (CF) using a two-phase participatory methodology. In phase one, 11 participants took photographs of their workplace. In phase two, participants told the story behind their photographs through one-on-one interviews. The data were analyzed using NVivo12 software. The participants’ experiences with compassion fatigue stemmed from issues with foundational infrastructure at the Toronto Zoo. Specifically, the participants highlighted issues related to training, staffing, and resource availability and discussed their resultant effect on animal welfare. The participants described the importance of built and sustained trust in their jobs, both with each other and with the non-human animals under their care. While the Zoo’s motto is “One TZ”, the participants noted conflict between the public’s perception of the Toronto Zoo and how the organization cares for its staff. The additive effects of mental and physical exhaustion have led to disengagement from activities that once brought joy and difficulty staying focused while at work and home. The findings will enable the Toronto Zoo to provide comprehensive mental health support for their staff and allow participants, researchers, partner organizations, and the general public to discover more about compassion fatigue in the hope that the lessons learned will last a lifetime.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.016 | 0.016 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".